Scaling tool

ABSTRACT

The present application generally pertains to scaling of a production process to produce a chemical, pharmaceutical and/or biotechnological product and/or of a production state of a respective production equipment. Particularly, there is provided a computer-implemented method of scaling a production process to produce a chemical, pharmaceutical and/or biotechnological product, the scaling being from a source scale to a target scale, wherein the production process is defined by a plurality of steps specified by one or more process parameters controlling an execution of the production process, the method comprising: (a) retrieving: parameter evolution information that describes the time evolution of the process parameter(s); a plurality of recipe templates, wherein a recipe comprises the plurality of steps defining the production process, and wherein a recipe template is a recipe in which at least one of the process parameters specifying the plurality of steps is a parameter being variable and having no predetermined value at the outset; (b) receiving: a source setup specification of a source setup to be used for executing the production process at the source scale, the source setup specification comprising the source scale value: a target setup specification of a target setup to be used for executing the production process at the target scale, the target setup specification comprising the target scale value; a source recipe defining the production process at the source scale: at least one acceptability function defining conditions for the values of the process parameter(s) at the source scale and/or at the target scale; (c) simulating the execution of the production process at the source scale using the source setup specification, the source recipe and the parameter evolution information: (d) determining, from the simulation, one or more source trajectories for the process parameters), wherein a trajectory corresponds to a time-based profile of values recordable during the simulated execution of the production process; (e) performing a target determination step comprising: selecting a recipe template pertinent to the production process out of the plurality of recipe templates; providing an input value for the at least one variable parameter in the selected recipe template; simulating the execution of the production process at the target scale using the target setup specification, the selected recipe template, the input value for the at least one variable parameter and the parameter evolution information; determining, from the simulation, one or more target trajectories for the process parameters; comparing the source trajectory(ies) and the target trajectory(ies); computing, based on the comparison and on the at least one acceptability function, an acceptability score for the selected recipe template; computing an optimal value for the at least one variable parameter in the selected recipe template by optimising the acceptability score and/or computing an acceptable range for the at least one variable parameter, wherein values within the acceptable range yield an acceptability score above a specific threshold; (f) if there is at least another pertinent recipe template, repeating the target determination step for at least another pertinent recipe template; (g) selecting at least one of the plurality of recipe templates and corresponding computed value(s) for variable parameters) as target recipe based on the acceptability scores computed for one or more recipe templates.

TECHNICAL FIELD

The following description relates to processes for the production ofchemical, pharmaceutical and/or biotechnological products. Inparticular, aspects of the application relate to scaling a processacross two or more scales.

BACKGROUND

Processes for the production of chemical, pharmaceutical and/orbiotechnological products are scale dependent; in other words, a processbehaves at least partly differently on a small scale (e.g., in alaboratory) in comparison to a large scale (e.g., in production).Usually the process is first performed at small scales and then atsuccessively larger scales.

However, at each scale transition, there is a risk that processperformance will be lost. This loss could be a catastrophic failure atthe larger scale, or simply a reduction in product quality or titre. Theproblem of process performance arises because it is not possible to keepall process and physical parameters constant across the scales. Forexample, mixing time, which is an important driver in terms of themicroenvironment seen by cells in a bioreactor, tends to increase withscale. To compensate for this, larger stirrer speeds could be selectedat the larger scales. However, this would dramatically increase thespecific power input, which in itself may be detrimental to the cells orproduct. Similarly, at the smaller scales, evaporation and samplingcomprise a larger proportion of the bioreactor volume than at the largerscale.

Thus, there is a problem relating to how to best scale up a process thathas been found to be optimal at the small scale, or, in other words, howto best translate a process from a source scale to a target scale,possibly passing through intermediate target scales.

In current approaches to translation between scales a scale-independentparameter is used as an intermediary or link between different scales.Starting from a set of known parameters for the source setupconfiguration (such as stir speed, fill volume and gassing rate for abioreactor), a scale-independent parameter (e.g. power input per volume)is derived by combining the known parameters. Then a given parameter isset for the target setup configuration (e.g. a desired fill volume).Finally, the remaining parameters for the target setup configuration(e.g. stir speed and gassing rate) are calculated to match thepreviously obtained value for the scale-independent parameter.

However, the above-illustrated approach for scale conversion is affectedby several issues:

-   -   It uses only a single scale-independent variable as intermediary        between the scales, whereas these processes require a careful        compromise between a plurality of variables.    -   It deals with scale translations as if they were only        single-step translations, i.e. from one source scale directly to        a single target scale, whereas often scale translation occurs        across multiple scales (a so-called “scaling train”). In other        words, in typical approaches, each translation considers only        two scales at a time, without considering possible subsequent        translations. Thus, subsequent transitions (e.g. to even larger        scales) may then be problematic if the first transition leads to        a “dead end” because no account was taken of the end goal. A        “dead end” is a configuration from which subsequent scaling is        risky in terms of the prospect of failed translation. For        example, translation from a small to an intermediate scale might        favour a very low stir speed at the intermediate scale (for        example, to maintain low shear in the cell environment), but        such low stir speed may then not be translatable to the large        scale because of the consequences for mixing time.    -   It fails to take into account the fact that the consequences of        a difference in variables between scales may not be symmetric.        For example, mixing time considerations are highly asymmetric:        decreasing mixing time is not normally an issue, but increasing        mixing time is likely to be. Similarly, a process might be        immune to an increase in k_(L)a but not to a decrease in it.    -   It does not provide a means by which prior information about        what constitutes an optimum process (e.g. a desire to reduce        energy input at large scales, a desire to sample frequently at        small scales) can be integrated with the need to match certain        conditions between scales. This prior information includes two        aspects. First, goals in terms of constraints in how it is        desirable to run the bioreactors. For example, a user may want        to run the bioreactors with particular constraints e.g. between        5% and 95% maximum stir speed. Second, knowledge about the        sensitivities of the organism or the process e.g. a particular        organism or process may be highly oxygen demanding, highly pH        sensitive etc. and translation needs to take this into account.        Similarly, a particular organism, product or process may be        shear sensitive and therefore this needs to be taken into        account in terms of tip speed/eddy size/etc when translating. In        typical approaches, prior process knowledge is not taken into        account when scaling. It is rather introduced post hoc,        resulting in unrealistic process parameter values.    -   It provides a binary outcome regarding one or more parameters at        the target scale, i.e. either good/acceptable or        bad/unacceptable. In reality there is a range of possible values        to which a lower or higher risk of deteriorating the process        performance is associated. In other words, the prior art        approach does not provide means to investigate sensitivity of        the process to one or more parameters    -   It does not provide a means of scaling processes from one scale        to another by taking into account the process as a whole,        focussing instead on single timepoints within a process.        Optimisation of certain timepoints within a process may be        detrimental for other timepoints, if the process is not        considered in its entirety.

Accordingly, there is a need for a scaling approach that reduces therisks associated with scale transitions. In particular, there is a needfor identification of appropriate process conditions at large scale toreduce risks related to poor process performance at successively largerscales.

SUMMARY

It is an object of the invention to transfer a process (for theproduction of a chemical, pharmaceutical and/or biotechnologicalproduct) from a source scale to a target scale in a manner thatmaximises the similarity of the process between the scales in terms ofsuccessfulness of the process. The process is similar at differentscales if important predictors of performance (for example in terms ofproductivity or titre) as determined from prior knowledge are themselvessimilar between source process and target process.

For example, if a process at a source scale resulted in a low percentageof dissolved oxygen (DO) throughout the process, a good translation atthe target scale would typically maintain a low percentage of DOthroughout the process, for example by adjusting stir speed or gassing.The percentage of DO is known to be usually a predictor of theperformance of the organism and also of productivity and quality. Evenif a higher DO percentage may, in some cases, be better in terms ofproductivity or quality at the target scale, a scaling leading to higherDO percentage would not be considered good because the similaritybetween the processes is lower. However, if the organism is known to beinsensitive to the DO percentage, the similarity between process will beevaluated based on other aspects that actually influence the product.

In particular, the scaling should not just optimize the outcome of theprocess at the target scale, e.g. quality and yield of the product.Rather, also the process itself is optimised in terms of similaritybetween different scales. Accordingly, the best match between theprocess at the source scale and the process at the target scale is foundboth from the perspective of the process itself and of the product.

In other words, it is an object of the invention to identify how to runa process at a given scale (e.g. a larger scale) to maximise the chanceof obtaining the same performance obtained at another scale (e.g. asmaller scale). It is also an object of the invention to identify how torun a process at a given scale (e.g. a smaller scale) given anticipateddeployment at another scale (e.g. a larger scale). Particularly, it isenvisaged identifying the range of process variants that can be run atthe smaller scale with a reasonable expectation of them performingsimilarly at a larger scale or scales. Thus, when a process is optimised(by experimentation, within a determined range) at small scale, theoptimised process then translates back well to larger scale.

It is another object of the invention to identify potential issues witha process in silico prior to deployment on hardware, and to find processalternatives which overcome these issues.

The achievement of these objects in accordance with the invention is setout in the independent claims. Further developments of the invention arethe subject matter of the dependent claims.

According to one aspect, a computer-implemented method of scaling aproduction process to produce a chemical, pharmaceutical and/orbiotechnological product is provided.

Examples of processes according to the present application areindustrial processes, particularly biopharmaceutical processes. Otherexamples include research and development processes or scientificresearch.

Examples of inputs or ingredients for a production process according tothe present application may include biological material, i.e. materialscomprising a biological system, such as cells, cell components, cellproducts, and other molecules, as well as materials derived from abiological system, such as proteins, antibodies and growth factors.Further ingredients may include chemical compounds and varioussubstrates.

Examples of inputs may include gasses and liquids. Gasses are any or allof air, oxygen, nitrogen, oxygen-enriched air and carbon dioxide.Liquids are typically:

-   -   media (nutrient mix in buffer e.g. glucose+amino        acids+salts+water)    -   inoculum (organism at relatively high density in media)    -   base (used to modulate pH e.g. ammonium hydroxide solution)    -   acid (used to modulate pH e.g. HCl solution)    -   nutrient feed (high concentration nutrient mix in buffer)    -   inducer (modulates behaviour of organism).

A production process of the present application may involve chemical ormicrobiological conversion of material in conjunction with the transferof mass, heat, and momentum. The process may include homogeneous orheterogeneous chemical and/or biochemical reactions. The process maycomprise but is not limited to mixing, filtration, purification,centrifugation and/or cell cultivation. The production process mayinvolve chemical or biological reactions that take time to complete,e.g. 6 hours for an E. coli microbial batch and 60 days for a mammalianperfusion process.

In particular, “producing” a chemical, pharmaceutical and/orbiotechnological product indicates any processing of the inputs,including but not limited to modifying a state of any of the inputs(e.g. changing the temperature, oxygen content etc. thereof), combiningany of the inputs reversibly or irreversibly, using the inputs forcreating new material.

Possible products may include a transformed substrate, baker's yeast,lactic acid culture, lipase, invertase, rennet. Further exemplarybiopharmaceutical products that can be produced according to thetechniques described in the present application include the following;recombinant and non-recombinant proteins, vaccines, gene vectors, DNA,RNA, antibiotics, secondary metabolites, growth factors, cells for celltherapy or regenerative medicine, half-synthesized products (e.g.artificial organs). Various production systems may be used to facilitatethe process, e.g. cell based systems such as animal cells (e.g. CHO,HEK, PerC6, VERO, MDCK), insect cells (e.g. SF9, SF21), microorganisms(e.g. E. coli, S. ceravisae, P. pastoris, etc.), algae, plant cells,cell free expression systems (cell extracts, recombinant ribosomalsystems, etc.), primary cells, stem cells, native and gene manipulatedpatient specific cells, matrix based cell systems.

Exemplarily, the production process may be a batch process, in which aspecific amount of feed medium for feeding an organism is provide as aninitial condition and then a control period follows.

The production process may be a fed batch process. The fed batch processmay involve a culture in which a base medium supports initial cellculture and a feed medium is added to support further growth or productproduction, once the initial nutrients have been depleted. In otherwords, the fed batch process may involve a batch period followed by afeed period.

The production process may be a perfusion process, in which a batchperiod is followed by a feed period with continual removal of theproduct, e.g. by filtration.

Techniques described in the present application may be useful forbioreactor processes, and for processes carried out at other levels ofproduction.

The production process is defined by a plurality of steps specified byone or more process parameters controlling an execution of theproduction process.

The production process is defined by the sequence of steps that isperformed in order to arrive at the product. Some steps may occursimultaneously to one another and other steps may occur in a temporalsequence after one another. A step may correspond to an action carriedout during the production process, wherein the action may be passive,such as waiting for an event to occur (such as an increase in oxygenlevels due to inactivity of the culture), or active, such as causing anevent to occur (e.g. stirring or adding a fluid) or setting valuesand/or profiles for a given quantity.

Exemplarily, steps that perform actions within a production process in abioreactor may be typified by the following non-exhaustive list:

-   -   set a set point in terms of stirring, gas supply, gas mix,        temperature    -   perform a profiled change in terms of stirring, gas supply, gas        mix, temperature    -   add a selected liquid to bioreactor vessel    -   remove liquid from bioreactor vessel    -   specify the connection of particular fluids to the bioreactor        and the composition of such fluids.

Further, the process may comprise step types that describe the flow ofexecution, e.g. specifying how/when an event occurs, such as: repeat astep or steps until a condition is met and/or for a specified number ofiterations, choose between various options depending on state ofbioreactor, wait until a condition becomes true (e.g. wait untildissolved oxygen rises to indicate end of batch phase before startingfeed), perform a step or set of steps concurrently (e.g. perform pHcontrol at the same time as temperature control).

The execution of the plurality of steps is controlled by one or moreprocess parameters. Exemplarily, the plurality of steps may be specifiedby a plurality of parameters, with each step being defined by one ormore process parameters.

In other words, the production process may include (i.e. may beperformed according to) at least one process parameter that has aninfluence on performance of the process (e.g., product titer, qualityattributes) and the product produced by the process.

The process parameters control the execution of the process in the sensethat they influence the course of the process, but at least some of themmay also be in turn influenced by the process. Further, processparameters may influence each other.

Specifically, some of the process parameters may be controllable, i.e.the values of at least some of the process parameters may bespecifically adjusted prior to and/or during performing the process. Inother words, at least one of the process parameters can be set e.g. byan operator or by a control system. In particular, these parameters maybe the ones describing and/or governing a state and/or behaviour of theequipment used for the production process, e.g. a bioreactor. In thefollowing, these parameters may also be referred to as “recipeparameters”, because they can be set in recipes, as explained below.

Accordingly, the adjustable process parameters may be a proper subset ofprocess parameters. In the case of a bioreactor these may include butare not limited to:

-   -   one or more parameters related directly to interventions in the        bioreactor, e.g. amount of liquid to remove in a sampling step;    -   one or more parameters providing an input to a control loop,        e.g. the set point for oxygen in the bioreactor (a control loop        in the bioreactor system would then monitor oxygen and adjust        e.g. stirring or gassing to hit the set-point);    -   one or more parameters providing an input to a profile e.g. the        rate exponent in exponential increase of feed; and/or    -   one or more parameters specifying a condition to be met (e.g.        dissolved oxygen must reach 90% before fed phase can start).

Exemplarily, the recipe parameters for a bioreactor may be or includestir speed, temperature, gassing, liquid addition, sampling, relativeprofiles and indications about which liquids to add.

In particular, the adjustable parameters may be given a constant, fixedvalue expressed by a given number or a value expressed as a function ofan argument, wherein the argument may be another process parameter(having a constant value or a changing value) or another quantity, suchas time. Accordingly, the values for the adjustable parameters may beset at the outset of the execution of the production process or may bedynamically determined during the execution of the production process.For example, set points and profiles may be dynamically determined totake into account events arising during the production process.

The process parameters may further include one or more parametersdescribing the state of the production, which is of course at leastpartly determined by the behaviour of the equipment, as well as by thespecifics of the equipment and the inputs (e.g. organism) used. Thevalues of these parameters may be intrinsic to the production processand not directly adjustable. However, they may be indirectly adjustableby modifying factors that affect them, such as the recipe parameters.

The values of these parameters may change during the production processand, thus, in the following they may be referred to as “dynamicparameters”. The measured value of a dynamic parameter at a given timeduring the process (either executed in the real world or simulated)corresponds to what is usually referred to as “process value” or“process variable”.

For example, the dynamic parameters for a production process in abioreactor may be classified as:

a. calculable knock-on effects of the recipe parameters as a result ofthe geometry and capabilities of the bioreactor, e.g. tip speed, stirspeed as proportion of maximum bioreactor stir speed, superficial gasvelocity;b. knock-on effects that can be obtained from previous empiricalresearch e.g. k_(L)a, mixing time, power input, minimum eddy size;c. variables that can be calculated from those in point (b) togetherwith properties of the bioreactor e.g. power per unit volume;d. variables which, due to a control loop in the process, arise fromfeedback from simulated bioreactor properties and aspects of theprocess, e.g. gassing rate, gas mix and stir speed;e. variables that result from the dynamics of oxygen or other gasseswithin the bioreactor as affected by e.g. stir speed, gassing etc., e.g.DO, partial pressure of carbon dioxide (ppCO2);f. variables that result from the dynamics of the organism, as dictatedby an organism model and the other variables above and below (e.g. cellcount, cell activity, cell metabolism);g. variables that result from liquid addition or removal calculable fromthe recipe parameters, e.g. fill volume, and potentially a model forevaporation;h. variables that result from a combination of liquid addition andremoval and also the dynamics of the organism, e.g. concentrations ofanalytes.

Exemplarily, the process parameters comprise both recipe parameters anddynamic parameters.

Based on the above, examples of process parameters may include but arenot limited to: temperature (affects cell growth), volume, pH (affectscell growth), specific buffering capacity (affects rate of pH change),cell density, cell activity state (mean), cell metabolic state (mean),k_(L)a (affects oxygen transfer), Reynolds number (affects mixing timeand cell growth), Froude number, mixing time, power input, power inputper volume, stir speed, tip speed, tip speed as a proportion of maximumpossible tip speed in system, gassing rate, gassing mix, minimum eddysize (potentially affects cell health), superficial gas velocity,concentration of abstracted nutrients e.g. primary carbon source,secondary carbon source, waste products, product, base, acid, primarynitrogen source, secondary nitrogen source, inducer, key analytes e.g.product quality, cell debris, protein concentration (may affect foamproduction, for example), cell parameters e.g. cell subpopulations,bioreactor heterogeneity e.g. variation in temperature withinbioreactor, fluid dynamic properties e.g. proportion of time cellsinhabit high shear environments, proportion of bioreactor that isrelatively free of stirring (dead zones), proportion of bioreactor sweptby impeller per unit time, carbon dioxide and carbonic acid dynamics,antifoam concentration (interacts with k_(L)a and other gas transfer)and foam accumulation parameters (function of SGV and also proteinconcentration, for example).

The scaling of the process is performed between a source scale and atarget scale.

A scale particularly refers to a configuration, e.g. a size of a setupused for executing the production process, wherein the configurationdetermines, among others, the throughput and the costs of the productionprocess. Exemplarily, for a production process executed with abioreactor, the scale value may refer to the volume of the bioreactorand/or to one or more components thereof such as impellers (e.g. typeand/or size thereof). A range of scales at which production processesare typically executed includes 2 mL (e.g. in microfluidic examples),less than 15 mL, 15 mL, 250 mL, 2 L, 10 L, 50 L, 200 L, 1000 L and 2000L. Bioreactors operating at these scales include Sartorius products suchas Ambr®, UniVessel® and BIOSTAT STR®. The scales may be divided intothree groups: small scale, intermediate scale and large scale. Somescales may belong to more than one group. For example, 2L could be bothsmall scale and intermediate scale and 50 L could be both intermediatescale and large scale.

Scaling the process indicates that a process designed and/or tested at asource scale is adapted for a target scale so that the process is stillsuccessful. The successfulness of the process may be evaluated e.g. onthe basis of the amount of product produced (titre), the quality of theproduct (e.g. chemical composition, including glycosylation, and foldingpattern of protein) and the presence/absence of other factors in themedia causing difficulty in purification downstream. For example,quality attributes may be used to assess the successfulness, whereinquality attribute may be a physical, chemical, biological ormicrobiological property that should be within an appropriate limit,range, or distribution to ensure desired product quality.

When scaling the process, one or more of the plurality of steps may bemodified, in particular one or more of the process parameters, inparticular the recipe parameters, specifying the steps may be changed.Further, in some examples, the plurality of steps may be modified inthat one or more steps are added or removed, an action is modified e.g.by adding a dependence on a condition and/or the order of the steps ischanged.

The reasons for which production processes are performed at a givenscale and then scaled are the following. Executing a production processat large scales is expensive, e.g. more than 10000 Euros per run. Manyvariables contribute to success of production and these are not a prioriknown for each process. Therefore small scale experiments are performedto identify a producing organism (clone) and to optimise the productionprocess prior to transfer to large scale. A typical workflow accordingto which a production process is implemented is as follows:

-   -   very small scale (e.g. 15 mL or less): identification of clone        in representative process with a large number of trials (e.g. 48        to 1000);    -   small scale (e.g. 250 mL or 2L): refinement of process with an        intermediate number of trials (e.g. 24 to 96) with the purpose        of modifying the process until success factors are maximised;    -   intermediate scale (e.g. 50L): initial process transfer,        potential further process refinement;    -   manufacture scale (e.g. 1000L): repeated production of product.

At each stage, there is a degree of optimisation such as selection ofclones based on best performing clones or selection of best gassingconditions. In each stage transition, not all parameters can be matchedbetween scales because the translation is non-linear. In particular:

-   -   demands at each stage may be different e.g. the amount of sample        required is small at small scale (few tests) and larger (e.g. >1        mL) later, but actually smaller relative to total bioreactor        size or minimising energy input is not an issue at small scale,        may be an issue at larger;    -   opportunities may be different, in that it is cheaper to do a        large number of runs at small scale and also automated variation        of process parameters is relatively easy at small scale;    -   constraints may be different: accuracy of pH control/gassing is        potentially lower at smaller scale, the availability of        analytics increases with scales, the tolerance to intervention        (e.g. sampling) decreases towards manufacturing scale, aspects        of bioreactors at given scale change what can be achieved (e.g.        at larger scale, it is increasingly difficult to remove adequate        heat from microbial culture and/or mixing time tends to        increase).

In light of the above issues, there is a need to make small scales asrepresentative as possible of large scales, make each stage in processtranslation as low risk as possible (i.e. minimise the risk of change ofsuccess criteria) also taking into account the risk for subsequentsteps.

In some examples, the source scale may be smaller than the target scale,e.g. the source scale may be 250 mL and the target scale may be 2L. Inother examples, the source scale may be the same as the target scale,but there may be different constraints on the production process, e.g. ashorter process time may be desired. Alternatively, the scales may bethe same but the configuration of the equipment may be different, e.g. aBIOSTAT STR® 50 with a 3-blade impeller and a 6-blade impeller and aBIOSTAT STR® 50 with two 3-blade impellers. In such cases, one of theobjectives of the scaling may be to maximise the chance of obtaining atthe larger scale the same performance obtained at the smaller scale.

In yet other examples, the source scale may be larger than the targetscale. This may be the case if some operations for optimising theprocess can be done more quickly and at lower costs at a smaller scale.Thus, starting from an actually executed process at a larger scale, theobjective of the scaling may be determining a range of process variants(in terms of steps and/or parameters) that can be run at the smallerscale and then scaled back to the original larger scale with a goodperformance. For example, the scaled down process could be used toselect between different clones, i.e. the aim is to find the clones thatwill perform will when scaled up. Indeed, several clones can be testedat small scales (e.g. in the order of thousands at the <15 mL scale, orabout 50 at the 15 mL scale).

The method comprises retrieving parameter evolution information thatdescribes the time evolution of the process parameter(s).

The parameter evolution information characterises how the one or moreprocess parameters change with time, including initial conditions forthe process parameters. In particular, the parameter evolutioninformation may comprise relations empirically derived from previousexecutions of the production process and/or equations derived bytheoretical model about the evolution of the production process. Theevolution information may comprise the explicit dependence of one ormore parameters on time together with relations linking the processparameters to each other. The parameter evolution information may alsocomprise information about variables that do not directly specify thesteps of the production process, but that indirectly affect the processparameters and, hence, the execution of the production process.

Parameter evolution information may have a number of constituent partse.g. parameter evolution information related to cell dynamics, tobioreactor dynamics and/or to chemical reactions occurring within thebioreactor.

For example, the parameter evolution information may compriseempirically derived mappings between recipe parameters (such as stirringspeed, gassing rate and fill volume) and dynamic parameters (such asmixing time, k_(L)a and power input). Additionally or alternatively, theparameter evolution information may comprise theoretically-derived orempirically derived equations and starting points for a cell culturemodel.

The parameter evolution information may also describe events related andgoing beyond what may be strictly considered to be the productionprocess per se. In particular, the parameter evolution information mayalso describe what happens outside a bioreactor e.g. time evolution ofsamples taken, or in a secondary reactor vessel, or in a downstreamprocessing facility (e.g. a purification unit), and/or in a piece ofanalytics equipment.

In one example, the cell culture is modelled using a hierarchical set ofordinary differential equations describing “cell processes”, where anyindividual cell process describes, amongst other things, thedependencies of that cell process, i.e. how its rate depends on pH, DO,temperature, and concentrations of various nutrients, and the results ofthat process, i.e. the change that occurs in cell count, titre, pH, DOetc. as a result of that process being active.

A given cell process A is allowed to depend on one or more drivingprocesses, X, Y . . . such that the rate of A is computed and thenmultiplied by either the sum or product of the rates of X, Y. A givencell process may depend on no driving cell process or any number ofdriving cell processes, and a given cell process may drive no other cellprocess or any number of other cell processes.

In a very simple case, for example, where there is just one dependency(on temperature) and one consequence (cell growth), this amounts tosolving the differential equation:

$\frac{d\; \rho}{dt} = \frac{r_{g}{N\left( {{T - T_{opt}},T_{sens}} \right)}}{\rho}$

where ρ is cell density, t time, r_(g) maximum growth rate, Ttemperature, T_(opt) optimum growth temperature, T_(sens) indicates thesensitivity of the growth to the temperature, and N(x,s) denotes thevalue of a normal distribution with standard deviation s at x.

A more typical case would have considerably more dependencies (e.g.dependency on key nutrients) and consequences (e.g. reduction in DO dueto cell activity, elevation of temperature in the case of microbial cellactivity, reduction in quantities of nutrients etc). In addition, theremay be a number of such cell processes with an additive effect e.g.constitutive growth, death due to toxin presence, product accumulation,etc.

The parameter evolution information for the cell growth then amounts todescribing the “cell processes” parameters and dependencies on eachother. This may take several forms:

a. Tabulated data (such as could be present in a spreadsheet) wherebyeach of the cell processes has a row to itself, and within each row, theparameters for the various potential dependencies are supplied (withrespect to a hard-coded library of functional forms for thedependencies)b. Database tables, for example, where there may be tables for

-   DB_CELL_CULTURE_MODEL,-   DB_CELL_CULTURE_PROCESS,-   DB_CELL_CULTURE_PROCESS_LINK,-   DB_CELL_CULTURE_PROCESS_DEPENDENCY,-   DB_CELL_CULTURE_PROCESS_DEPENDENCY_PARAMETER,    where DB_CELL_CULTURE_MODEL catalogues the named models which can    then be referenced by the software, DB_CELL_CULTURE_PROCESS    catalogues the cell culture processes within any model,    DB_CELL_CULTURE_PROCESS_LINK relates entries in    DB_CELL_CULTURE_PROCESS together to indicate the fact that some    processes drive others, DB_CELL_CULTURE_PROCESS_DEPENDENCY indicates    particular dependencies (e.g. by indicating the trajectory variable    of the dependency and the form of the dependency), and so on.    c. Structured data formats such as XML, or equally JSON or a    proprietary form.

These data might then be stored in several ways:

a. Within the software responsible for performing scale conversion, forexample, embedded within a DLL or executableb. In files available to the s/w on a file system (a file might supplyany of these forms)c. Within a database instance

The data might further then be stored and accessed:

a. Locally to the software performing the conversion e.g. on the samefile system, or accessed by a database engine built into the s/w,potentially within memory, SD card, hard drive, CDROM, DVDROM etc.b. Within a file share on a network accessible to the softwarec. Across a client/server (e.g. webservice) system, with the clientphysically separated from the server, that is, located close to thesoftware (physically), or accessible across a network by the software,in the latter case either stored in one location or distributed acrossmultiple sites.

In addition to the cell culture, physical dynamics of the process aredescribed in the parameter evolution information. This comprises howparameters are related to each other at a point in time and how eachparameter evolves over time.

As with the cell culture model, the model for physical dynamics may bestored as XML, database tables, and so on and the substrate for the datacould be DVD, CDROM, hard disk etc., and the location of the data localor distinct, and the distribution of the data in one place ordistributed.

To summarize, the data representing the parameter evolution informationmay be retrieved according to a plurality of different implementations.In particular, the data may be already stored as such prior toretrieving or they may be computed on the fly when needed.

The method comprises retrieving a plurality of recipe templates.

A recipe comprises the plurality of steps defining the productionprocess. In other words, a recipe is a structured representation of theproduction process, e.g. of the activity of a bioreactor. This meansthat the plurality of steps are expressed in a structured manner, e.g.in a format that can be interpreted by a machine.

As already explained above, there are steps indicating an action(passive or active) and steps controlling the flow, e.g. sequence(execute the contained steps in sequence), repeat (repeat the containedsteps until a condition is met or a given number of times) and choice(depending on a condition, perform one step or another).

As discussed, the actual behaviour of the steps during (real orsimulated) execution, i.e. what happens is determined by one or more ofthe process parameters, wherein these include both recipe parameters anddynamic parameters. However, the recipe may comprise only the recipeparameters, i.e. those that can actually be set in order to run theprocess. The process parameters may be expressed as numbers, asalgebraic expressions or as functions of other variables.

Accordingly, the recipe particularly comprises the plurality of steps aswell as one or more values for the recipe parameters specifying thesteps. In other words, the recipe particularly provides a well-definedprocedure that can be directly implemented when executing the productionprocess and that dictates how the process equipment is controlled withtime. The values may be dynamically fed to the recipe during execution,but in any case it is predetermined which values will be fed.

Conversely, a recipe template particularly is a recipe in which at leastone of the recipe process parameters specifying the plurality of stepsis a parameter being variable and having no predetermined value at theoutset.

Accordingly, a recipe template involves one or more degrees of freedomon how to execute the production process. Different values for the atleast one variable parameter results in different recipes being producedfrom a given template. The difference may be in the values of theprocess parameters only or also in the sequence of steps, e.g. if a pathwithin a template is contingent on a variable parameter. Multipleparameters might be free within one recipe template.

Exemplary parameters that would be free to vary (i.e. variableparameters) may include but are not limited to one or more of thefollowing:

-   -   Concerning attachments to the bioreactor system        -   Concentration of primary carbon source in batch media        -   Concentration of primary nitrogen source in feed media        -   pH of the acid attached for top control of pH        -   buffering capacity of the batch media        -   density of cells in the inoculum        -   percentage of oxygen in oxygen-enriched air    -   Concerning profiles and set-points        -   The supply rate for constant gassing in batch phase        -   The rate at which stir speed is incremented over time in the            batch phase        -   The P or I parameter in a PI feedback loop for gassing            control        -   The maximum air flow rate before oxygen supplementation            occurs        -   The exponential coefficient in an exponential profile for            feed        -   The initial feed rate in an exponential profile for feed        -   The duration of a plateau phase in a feed profile (for            example, after an exponential period)        -   The rate of temperature drop during a temperature shift for            induction        -   Temperature setpoint during batch phase    -   Concerning discrete events in recipes        -   Bioreactor fill volume (as a proportion of total bioreactor            volume)        -   Inoculum volume (as a proportion for example, of bioreactor            fill volume)    -   Concerning recipe structure and flow control    -   The cascade order (e.g. stirring then gassing then O2        supplementation; or gassing then stirring then O2        supplementation) in DO control    -   Threshold for primary carbon source and/or dissolved oxygen (DO)        to initiate fed phase    -   Frequency of sampling    -   Volume of sample each time a sample is taken    -   Threshold primary carbon source in sample to cause feed        supplementation    -   Threshold cell density to initiate harvest.

Different recipe templates may be used at granularity of differentorganisms, clones, or production processes, deployment systems (e.g.disposable v stainless steel reusable production bioreactors). There maybe further refinements if there is feedback that scaling works better orworse with particular recipe templates.

Recipe templates might also be shared between organisations orcategorised on the web in a repository. They might be selected based onuser feedback with respect to scaling successes or failures.

Recipe templates may be part of a library and may be retrieved similarlyto the parameter evolution information. For example, recipe templatesmay be stored in a structured data format (e.g. persisted by aserialiser in XML). Recipe templates could equally be stored in adatabase, spreadsheet, and stored locally, in an organisation filesystem, in a database within an organisation or on the cloud (remotely).

Recipe, and, thus, recipe templates as well, may comprise marksidentifying specific parts of a production process. Exemplarily, abeginning mark and an end mark may enclose a portion of the process. Themarks may distinguish portions of the production process that are e.g.more relevant or critical when scaling, as will be explained withreference to the acceptability functions and trajectory comparisonbelow.

Both the parameter evolution information and the recipe templates may beused in a plurality of different scale translations, provided that theproduction process in question is covered by the steps and the processparameters considered in the parameter evolution information and therecipe templates.

Other inputs required by the method may instead be provided for eachspecific translation. Indeed, the method further comprises receiving: asource setup specification of a source setup to be used for executingthe production process at the source scale, the source setupspecification comprising the source scale value; a target setupspecification of a target setup to be used for executing the productionprocess at the target scale, the target setup specification comprisingthe target scale value; a source recipe defining the production processat the source scale.

A setup specification includes information about the setup of theprocess equipment used for executing the production process, in primisthe scale value of the equipment, e.g. the capacity of a bioreactorexpressed in litres. Further, the setup specification may include atleast one of the components of the setup and its characteristics, e.g.specifying that the equipment comprises an impeller and, optionally,which kind of impeller and so on. Also, other characteristics of theequipment may be indicated by referring to a model of a product, e.g.Sartorius Ambr®. In other words the setup specification describes theequipment used for executing the production process at a given scale, inparticular providing information necessary for simulating the process,as will be discussed below.

In particular, the setup specification may specify values for one ormore process parameters (e.g. maximum fill volume, minimum fill volume,maximum stirrer speed, maximum gassing rate, minimum gassing rate, lowerimpeller height, upper impeller height, liquid cross sectional area)that must be fed to a recipe prior to deploying the recipe and/oraccording to which specific parameter evolution information or recipetemplates may be selected.

Examples of a source setup specification and target setup specificationare, respectively: Ambr® 250 bioreactor with standard sparger andmammalian impeller, and Sartorius SIR® 50 with ring sparger and two3-blade impellers.

In addition, a source recipe is provided. In view of the definition ofrecipe given above, the source recipe is none other than a recipecomprising the plurality of steps (and relative values for processparameters) that define the process as it occurs at the source scale.

An example for a source recipe may correspond to the followingprocedure: “Fill bioreactor with 0.2L of given media, heat to 35degrees, inoculate with clone to a density of 1e6 cells mL-1, incubatestirring at 600 rpm for 36 hrs controlling pH to 7.4 with bottom and topcontrol i.e. addition of acid or base as needed to push pH back to 7.4;maintain temperature; gas at a rate of 0.1 of total volume per minutewith air; feed with complex feed for 36 hrs continuing to monitor andcontrol pH, temperature; control DO with stirring and gassing, addinducer to trigger production. Harvest after 36 hrs.”

Further, the method comprises receiving at least one acceptabilityfunction defining conditions for the values of the process parameter(s)at the source scale and/or at the target scale. Acceptability functionsmay define conditions both for the recipe parameters and dynamicparameters.

An acceptability function is a parameterised function mapping from aprocess parameter or a combination of process parameters to a valueindicating acceptability. The value for acceptability may be a realnumber between 0 and 1, i.e. equal to 0 or 1 or greater than 0 and lowerthan 1. Accordingly, the conditions defined for the values of theprocess parameters may be binary conditions, in the sense that a valueis either considered accepted or not accepted, but they may also be morenuanced conditions, in which a degree of acceptability for a given valueis expressed. In other words, the acceptability function may expressprecise constraints on which values are allowed and/or indicate howfitting a given value is considered to be.

Exemplarily, an acceptability function may fall into two categories,absolute and relative.

A relative acceptability function provides an evaluation of howacceptable a value of a process parameter is when considering the valuein relation to other quantities, exemplarily the value for the sameprocess parameter at another scale. In particular, the relativeacceptability function may consider the value of the process parameterat the source scale and at the target scale. The source value and thetarget value may be put in relation to one another in different manners,e.g. considering the difference or the ratio.

An example of a relative acceptability function maps the differencebetween the source value and the target value for the mixing time to 0if the mixing time is less at the source than at the target, and to 1otherwise. Another example maps the difference between the power pervolume (PPV) values at source and target scale to a normal distribution.

In other examples, the acceptability function may be a function of twoor more process parameters. For example, the difference of the productof cell density and cell activity between source scale and target scalemay be mapped to a normal distribution.

Conversely, an absolute acceptability function provides an evaluation ofhow acceptable a value of a process parameter is when considering thevalue on its own.

An example of an absolute acceptability function maps a stir speedbetween 0% and 5% or between 95% and 100% of a maximum possible stirspeed (given the target bioreactor) to 0, and a stir speed between 5%and 95% of the maximum possible stir speed to 1. Another example mapsthe PPV to a normal distribution around a given maximum.

Exemplarily, the at least one acceptability function may be a pluralityof acceptability functions comprising at least one absoluteacceptability function and at least one relative acceptability function.

The acceptability functions may be grouped according to the scales towhich they apply, e.g. according to three groups: a small scale group,an intermediate scale group and a large scale group. As mentioned above,certain scales may fall into more than one group. There may be more thanthree distinct groups of scales.

The setup specifications, the source recipe and the one or moreacceptability functions may be received as input from an external source(e.g. a user and/or a control system configured to execute theproduction process).

The method further comprises simulating the execution of the productionprocess at the source scale using the source setup specification, thesource recipe and the parameter evolution information; and determining,from the simulation, one or more source trajectories for the processparameter(s), wherein a trajectory corresponds to a time-based profileof values recordable during the simulated execution of the productionprocess.

The simulation of the execution of the production process is animitation of the execution of the production process in the real worldperformed by means of a computer system. The source setup specificationand the source recipe provide initial conditions for the simulation anda description of the process to be simulated, while the parameterevolution information models the evolution of the process with time.

In particular, it is possible to derive values of a process parameter atthe different times during the evolution of the process in order toobtain a trajectory. Accordingly, a plurality of source trajectories maybe obtained corresponding to a plurality of process parameters asevolved when performing the process at the source scale. In someexamples, trajectories may be determined both for recipe parameters anddynamic parameters. In other examples, trajectories may be determinedonly for dynamic parameters.

Each trajectory may be understood to summarize and provide an overviewof the associated process parameter. Each trajectory may be implementedas a curve or graph that describes the time evolution of the processparameter during the simulated execution of the production process. Inparticular, each trajectory may comprise a plurality of pointsrepresenting values of a parameter corresponding to different moments intime. For example, a time unit between successive points may be onehour.

The method further comprises performing a target determination step inwhich the execution of the production process is simulated at the targetscale. In order to perform the simulation, one of the plurality ofrecipe templates is selected and an input value for the at least onevariable parameter in the selected recipe template is provided.

The combination of the selected recipe template and the input value forthe variable parameter provides a recipe for the target scale that canbe used for the simulation, similarly to how the source recipe is usedfor simulating at the source scale. When there is a plurality ofvariable parameters, a corresponding plurality of input values isprovided, i.e. an input value for each variable parameter, so that therecipe is fully specified.

The recipe template may be selected among all the available recipetemplates or within a subgroup of recipe templates pertinent for theparticular scaling. “Pertinent” here might mean: appropriate fordeployment process or appropriate for organism based on organisationalknowledge and/or process knowledge. The selection may be performed by auser or automatically according e.g. to flags present in the recipetemplates and indicating their suitability.

The input value may be an educated guess based on process knowledge andit could be part of a set of possible values linked to the specificrecipe template. For example, the recipe template may comprise one ormore candidate values together with a testing range for each value, sothat the input value may be chosen as any value lying in an intervalaround a candidate values. The candidate values may, thus, be retrievedtogether with the recipe templates. Alternatively, the input value maybe supplied by a user.

The execution of the production process at the target scale is simulatedusing the target setup specification, the selected recipe template, theinput value for the at least one variable parameter and the parameterevolution information. Similarly to what is done for the source scale,one or more target trajectories for the process parameters are thendetermined.

The target determination step further comprises comparing the sourcetrajectory(ies) and the target trajectory(ies). If only one sourcetrajectory for a given process parameter is determined, only thecorresponding target trajectory for the same process parameter may bedetermined, and the two may be compared. If a plurality of trajectoriesfor the source scale and the target scale are determined, thetrajectories are compared pairwise, i.e. the source trajectory for agiven process parameter is compared with the target trajectory for thesame process parameter.

Exemplarily, the comparison may be carried out by comparing a point onthe target trajectory with a corresponding point on the sourcetrajectory. Each trajectory represents a numerical description of aprocess parameter over time, so that each point on the trajectory is avalue of a process parameter at a particular time.

The comparison may e.g. comprise calculating the difference between apoint value in the target trajectory and a corresponding (i.e. at thesame time) point value in the source trajectory for the same parameter.Other quantitative assessments of the comparison may be performed, suchas taking a ratio of the values or combining the values according togiven relations.

The target determination step further comprises computing, based on thecomparison and on the at least one acceptability function, anacceptability score for the selected recipe template. In particular, theacceptability score is assigned to the combination of the selectedrecipe template and provided (initial) value(s) for the at least onevariable parameter.

The acceptability score indicates a degree of compliance of theproduction process at the target scale with the conditions of theacceptability function. In other words, the acceptability score providesan evaluation of the combination of the recipe template and value(s) forthe variable parameter(s) according to the criteria of acceptability setin the acceptability function(s).

As explained above, the acceptability functions map the processparameters or a combination thereof to acceptability values.Accordingly, one or more applicable acceptability functions are appliedto the target trajectories of corresponding process parameters in orderto obtain an acceptability score for the combination of the recipetemplate and value(s) for the variable parameter(s). In other words, thesuitability of the recipe template plus values for variable parametersfor executing the process at the target scale is assessed via theprocess parameters trajectories.

By “applicable” is meant that the acceptability function definesconditions for the process parameter to which the source and/or targettrajectory corresponds. It should be noted that the process parameter ofthe trajectories may or may not coincide with the variable parameter. Inother words, the acceptability score may express an assessment on thevalue for the variable parameter in an indirect manner, provided thatthere is a relation between the trajectory process parameter and thevariable parameter, i.e. that they are not completely independent fromeach other.

In particular, when an absolute acceptability function is applied to atarget trajectory, the comparison with the source trajectory may not beneeded, i.e. “based on the comparison” may be an optional feature forthe computing of (at least part of) the acceptability score.

In other words, the absolute acceptability function may be applied tothe values corresponding to the points in the trajectory. When arelative acceptability function is applied to the target trajectory, theacceptability function may be applied to the comparison between thetarget trajectory and the corresponding source trajectory. In otherwords, the relative acceptability function may be applied to thecomparison between values corresponding to the points in thetrajectories, such as the pairwise differences. Further, there may beacceptability functions with higher dimension that require a comparisonbetween multiple source trajectories (e.g. pH and DO) and corresponding(i.e. also pH and DO) target trajectories.

Specifically, if there is only one applicable acceptability function,this is applied to the target trajectory to obtain the acceptabilityscore. In some implementations, this comprises calculating anacceptability value for each time point in the target trajectory andaggregating the acceptability values at different time points to obtainthe acceptability score. For example, the aggregation is done byconsidering the mean (arithmetic or geometric) or the median or theproduct of the acceptability values, e.g. the acceptability score may beS1=

S(t)

_(t). Other combinations may be possible.

When there is more than one applicable acceptability function, theacceptability score obtained when applying a single acceptabilityfunction to the target trajectory may be a partial acceptability score.All applicable acceptability functions are applied to the targettrajectory and the partial acceptability scores obtained by each one ofthem are aggregated, wherein the aggregation may be done in any of theways discussed above. For example, the acceptability score may be S2=

S1(acc. func.)

_(acc.func).

If there is only one target trajectory, the acceptability score obtainedby applying all applicable acceptability functions represents theacceptability score for the target recipe as a whole.

When there is more than one target trajectory, the acceptability scoresmay be further aggregated. In other words, in case of a plurality oftrajectories, an acceptability score is computed for each targettrajectory and then these are aggregated for obtaining the acceptabilityscore for the selected recipe template and the provided value(s) for thevariable parameter(s).

Exemplarily, the scores obtained for different trajectories areaggregated by computing the exponential of the mean of the log of thescores across the trajectories. In other words, S3=exp(

log[S2(traj.)]

_(traj.)). This ensures that any process that is not performant for anypart of the culture (acceptability score=0) will have an aggregateacceptability score of 0, and any process performant perfectly for allof the culture (acceptability score=1) will have an aggregateacceptability score of 1. There are alternative ways of computing theaggregate acceptability score for the recipe template, such as the onesdiscussed above.

In some examples, in addition to the overall aggregate acceptabilityscore for the recipe template, being e.g. a real number x, with 0≤x≤1,an acceptability value may be computed for each time point in thetrajectory(ies) and the aggregation may be done separately for each timepoint. The result would be a function x(t).

To summarize, when a plurality of acceptability functions is receivedand a plurality of target trajectories are computed, computing theacceptability score may comprise: for each target trajectory of theplurality of target trajectories obtaining a second partialacceptability score by:

-   -   selecting one or more applicable acceptability functions;    -   for each applicable acceptability function performing a        calculation step of:        -   calculating an acceptability value based on the            acceptability function for each time point in the target            trajectories;        -   aggregating the acceptability values at the different time            points to obtain a first partial acceptability score;    -   if there is a single applicable acceptability function, setting        the second partial acceptability score to the first partial        acceptability score;    -   if there is a plurality of applicable acceptability functions,        aggregating the first partial acceptability scores for all        applicable acceptability functions to obtain the second partial        acceptability score;        and aggregating the second partial acceptability scores for all        target trajectories to obtain the acceptability score.

As explained above, recipe templates may comprise marks identifyingspecific parts of a production process. Exemplarily, a beginning markand an end mark may enclose a portion of the process. The marks allow anacceptability function to apply to part or all of a trajectory.

For example, in the recipe template the start of the batch phase and theend of the batch phase may be marked. When marks are present in therecipe template, all or a subset of the acceptability functions shouldbe applied e.g. only to the interval between start and end batch to thetarget trajectories. In other words, some acceptability functions may beapplied only to a phase, e.g. the batch phase, and other acceptabilityfunctions may be applied only to another phase, e.g. the fed phase. Forexample, during the batch phase, the gassing may be set to constant inthe recipe template and an acceptability function that makes theacceptability score dependent on the percentage of dissolved oxygen isapplied only to the batch phase interval in the trajectories, since thegoal is to ensure constant gassing results in similar dissolved oxygenenvironments during batch phase.

When a (relative) acceptability function is applied to a comparisonbetween source and target trajectories between two marks, time isoptionally scaled between the source and target trajectories so that theinterval between the marks is the same.

For example, in the simulation at the source scale, the batch phasestarts at t=2 and ends at t=12; at target scale, the batch phase startsat t=2 and ends at t=22. In trajectory comparison between these markseither: t=5 in the source trajectory is compared with t=5 in the targettrajectory (i.e. not scaled) or t=5 in the source trajectory is comparedwith t=8 in the target trajectory (i.e. scaled, each unit in target isworth 2 in the source, 3 time units have passed between start of batch).

Finally, the target determination step comprises computing an optimalvalue for the at least one variable parameter in the selected recipetemplate by optimising the acceptability score and/or computing anacceptable range for the at least one variable parameter, wherein valueswithin the acceptable range yield an acceptability score above aspecific threshold.

The acceptability score computed as explained above yields a numberwhose value is dependent, among others, on the value(s) assigned to thevariable parameter(s) in the selected recipe template. Thus, theacceptability score may be seen as a function of the one or morevariable parameters, hereafter called “score function”. In the case of aplurality of variable parameters present in the selected recipetemplate, the score function is a multi-variable function of thevariable parameters.

The score function may be optimised to find the best input values forthe variable parameters, i.e. those values for which the process at thetarget scale is most similar to the process at the source scale and mostsuccessful according to the criteria specified by the acceptabilityfunctions. Finding the optimal value(s) for the variable parameter(s) isan optimisation problem in which the score function is maximised orminimised by systematically choosing input values from within an allowedset and computing the value of the score function. Depending on thenumber of variable parameters, the optimisation problem may be amulti-dimensional problem. The allowed set may be specified in therecipe template, as mentioned above, or may be otherwise determined. Insome cases, there may be more than one optimal value that optimises thescore function. If the score function assumes real values between 0 and1, and 1 is assigned to perfect acceptability, then the score functionmust be maximised. Examples of optimisation algorithms are theNelder-Mead method and the steepest descent method. The result ofoptimising is that the parameter value or the combination of parametervalues that give the best (e.g. highest score) for the selected recipetemplate is found. In some cases, it may be necessary to use multiplestarting values for optimisation, as the space being explored may behighly non-linear and may exhibit multiple local optima.

In addition or alternatively to finding the optimal value(s) for thevariable parameter(s), acceptable range(s) may be found by constrainingthe score function to assume a value (i.e. the acceptability score)above a specific predetermined or predeterminable threshold. There maybe more than one acceptable range for a given variable parameter. Thethreshold may be input by a user or may be set or derived based on thebest value obtainable for the acceptability score, e.g. a fraction ofthe best value such as 70%, 80% or 90% or set based on absolute criteriaon the acceptability score, e.g. it has to be at least 0.5 or 0.6 or0.7. In particular, the term “range” should be construed broadly toencompass multi-dimensional results.

Accordingly, the result of the target determination step may be a singlepoint in the variable parameter space and/or a curve or surface in thevariable parameter space. Mathematically speaking, this will correspondin the exemplary case to the union of a set of arbitrary dimensionalmanifolds.

However, as explained above, a given acceptability score is not just theresult of the input values fed to a selected recipe template, but alsoof the choice of the recipe template itself. Therefore, the targetdetermination step may be repeated for at least another recipe template,if there is more than one pertinent recipe template. Accordingly, thestep of “repeating the target determination step for at least anotherpertinent recipe template” may be optional. In particular, the targetdetermination step may be repeated for all recipe templates in thelibrary or only for the recipe templates in the subset of pertinentrecipe templates discussed above.

The method further comprises selecting at least one of the plurality ofrecipe templates as target recipe based on the acceptability scorescomputed for one or more recipe templates. In particular, the one ormore recipe templates selected as target recipe are selected togetherwith the optimal input value(s) and/or the acceptable range(s) for theinput value(s).

Once the target determination step has been completed for all the recipetemplates for which it is supposed to be performed, each recipe templatehas an (optimal) acceptability score associated with an optimal inputvalue and/or a plurality of acceptability scores above a given thresholdassociated with a range of input values.

Therefore, one or more candidates for the target recipe, which willspecify how to execute the process at the target scale for the actualdeployment, may be chosen among the combinations recipe template plusinput value(s) based on the acceptability scores computed whenoptimising and/or when setting a minimum threshold. In some examples, asingle target recipe may be selected while in other examples a pluralityof target recipes may be given. In particular, a single recipe templatewith a plurality of acceptable input values for a single variableparameter (e.g. more than one optimal value or an acceptable range) mayoriginate a plurality of target recipes. Similarly, a plurality ofrecipe templates with good acceptability scores may originate aplurality of target recipes.

According to an exemplary implementation, the method may furthercomprise outputting the one or more target recipes in the form of recipetemplate(s) plus input value(s). Additionally, the corresponding “best”predicted trajectory(ies) at the target scale and/or the acceptabilityscore(s) may be output. In particular, if also the time-dependentacceptability score x(t) has been computed, as discussed above, thiscould also be output.

In some implementations, the target recipe(s) obtained by theabove-explained selection may be cast in a form that an automated systemcan handle, for example an “experiment protocol” on Ambr® 15 or Ambr®250. The method may further comprise providing the target recipe to atarget control system; and executing, by the target control system, theproduction process at the target scale based on the target recipe. Theidentified scaled process conditions may be transferred either manuallyor automatically between bioreactor configurations.

In other words, the target recipe(s) with relative input value(s) asselected by the above-described method may be fed directly to a targetcontrol system configured to control the target setup equipment to carryout the process at the target scale. If more than one target recipe (seeabove) is provided to the target control system, the target controlsystem may select only one target recipe e.g. based on certain priorssuch as existing configuration of the target setup, or may execute theproduction process multiple times.

When the production process is executed in the real world at the targetscale, the results may be used to provide feedback. In someimplementations, the method may further comprise evaluating theperformance of the production process at the target scale; and modifyingthe at least one acceptability function based on the evaluation.

The performance of the production process may e.g. be evaluated by usingthe quality attributes defined above. Modifying the at least oneacceptability function based on the evaluation may include assigningweights to the acceptability functions, so that they become more or lessrelevant in determining the acceptability score. For example, if thescaling resulted in a good performance, the acceptability functionsinvolved may be given a greater weight. Conversely, if the scalingresulted in a poor performance, the acceptability functions involved maybe given a lower weight. In other examples, the form of theacceptability functions may be modified, e.g. by substituting a normaldistribution with a different distribution or by changing the range ofvalues of a process parameter that are mapped to 0 by the acceptabilityfunction.

Another example for feedback is to use the predicted target trajectoriesand the actual trajectories and modify the parameters for the simulationwhen there are discrepancies between the predicted and actualtrajectories. The means of modifying these parameters is that ofnon-linear fitting i.e. minimisation of differences between observed andexpected.

Yet other feedback mechanisms may be implemented, such as in order toimprove the parameter evolution information.

In some implementations, the source recipe defining the productionprocess at the source scale may be obtained by:

defining an aim quantity characterizing the production process at thesource scale;executing, by a source control system, the production process multipletimes at the source scale while varying the process parameters and/orthe process steps;selecting, for defining the source recipe, a process based on the resultfor the aim quantity given by the process parameters and the processsteps.

The aim quantity may be related to the product and/or to other aspectsof the process. For example, the aim quantity may be one or acombination of the quality parameters, or may be defined otherwise. Theproduction process may be repeated a plurality of times, since e.g. atsmall scale it is not too expensive, in order to find the bestcombination of process steps specified by given process parameters,wherein the best combination is the one that optimises, e.g. maximises,the aim quantity.

Examples of aim quantities that would be maximised include but are notlimited to one or more of the following:

-   -   Total quantity or quantity per unit volume    -   Quality (chemical composition of product, protein structure        (primary, secondary and tertiary structures), glycsolyation        patterns)

Purity (quantity of product relative to similar molecules)

-   -   Release of product from cell wall, cytoplasm or other cell        compartments,    -   Release of cell lysis being beneficial for product release into        media    -   Throughput (that is, the inverse of cycle time, that is,        minimising culture period)    -   Ability to detect process deviations early and correct (e.g.        might point towards increased sampling)

Examples of aim quantities that would be minimised include but are notlimited to one or more of the following:

-   -   Cell debris    -   Presence of molecules, cell components or cell that interfere        with purification    -   Shear or chemical damage to product    -   Media cost (either total media used, or expensive components of        media)    -   Energetic cost (especially at larger scales)    -   Risk of process failure (i.e. the degree to which small        fluctuations e.g. in bioreactor or cell performance might push        the process out of the operational region)

The recipe-scaling method illustrated above for scaling from a sourcescale to a target scale can be generalized for the so-called trainscaling, i.e. for scaling from a source scale to a final target scalepassing through one or more intermediate target scales. Conventionally,each transition in the train scaling is treated separately. Instead,according to the present invention and in particular thanks to theacceptability function, the train scaling is performed considering allpassages from one scale to another at the same time. In other words, thetrain scaling is treated as a single, composite process instead ofartificially separating it into pairwise scalings. Thus, the chances ofsuccessful performances of the process at any subsequent scale areincreased.

The method as described above is naturally extended to the train scalingscenario. When the scaling method is applied to a train scalingcomprising one or more intermediate target scales, one or moreadditional simulations need to be run for the one or more intermediatetarget scales, which requires corresponding intermediate target setupsand, especially, corresponding input values for the variable parametersin the recipe templates. Indeed, one of the goals of the method fortrain scaling is to obtain a target recipe for each of the intermediatetarget scales and the final target scale. This means that, once apertinent recipe template is selected, its one or more variableparameters are tentatively populated by one or more sets of inputvalues, respectively. In other words, for each variable parameter, aplurality of input values, each corresponding to a target scale, isprovided. For example, taking a case with two intermediate target scalesbetween the source scale and the final target scale, a first, second andthird input values are provided for the variable parameter; the firstinput value may provide an educated guess for the value of that variableparameter at the first intermediate target scale, the second input valuemay provide an educated guess for the value of that variable parameterat the second intermediate target scale and the third input value mayprovide an educated guess for the value of that variable parameter atthe final target scale. Accordingly, the simulation run for each scaleis based on the combination of the recipe template and the correspondinginput value provided for that scale.

Based on the plurality of simulations, trajectories for the intermediatetarget scales are determined, in addition to those for the source andfinal target scales. The trajectories at different scales are thencompared pairwise and also in higher-cardinality combinations (e.g.three trajectories at three different scales are compared together orfour trajectories at four different scales are compared together and soon). For example, the comparison of three trajectories may e.g. comprisecalculating the difference between a point value in the final targettrajectory and the sum of corresponding (i.e. at the same time) pointvalues in the source trajectory and in the intermediate targettrajectory for the same parameter. Other quantitative assessments of thecomparison may be performed.

The at least one acceptability function for the train scaling may, inparticular, define conditions for the values of the process parametersat any single scale and/or at any number of scales. In particular, theremay be absolute acceptability functions defining conditions for theprocess parameters at any single scale taken alone and/or there may berelative acceptability functions defining conditions on relationsbetween values at two or more different scales. The score will in thiscase be derived as an aggregate of the output of acceptability functionsfrom a plurality of scale combinations.

Finally, similarly to the two-scale translation, an optimisation problemis solved. The difference is that the dimensionality of the optimisationproblem is higher in the train scaling. If a simple example in which theselected recipe template has only one variable parameter is considered,the dimensionality of the optimisation problem of the acceptance scorefunction for that recipe template is one. In the case of train scaling,the dimensionality is increased depending on the number of intermediatetarget scales. If there is one intermediate target scale, theoptimisation process has to find at the same time an optimal value forthe variable parameter for the intermediate target scale and an optimalvalue for the variable parameter for the final target scale, i.e. thepresence of one intermediate target scale has increased of one thedimensionality of the problem. If there are two intermediate targetscales, the dimensionality is increased by two, for the simple exampleabove. Generally, if the dimensionality of the optimisation problem inthe two-scale translation is D1 and the number of intermediate targetscales plus final target scale is T, the dimensionality of theoptimisation problem in the train scaling with T-1 intermediate targetscales will be D2=D1*T. Accordingly, the number of “constraints” imposedby the acceptability functions must be sufficient for making theoptimisation problem determined. This may translate for example intooptimising at least two acceptability score functions simultaneously,which may e.g. be combined by multiplication. It may also result in theoutput of the optimisation being a range of possibilities forintermediate scales, all of which share the maximal value of theaggregated score.

Similar considerations apply, with due differences, for the acceptableranges.

Therefore, at least one result of the method for train scaling is toprovide one or more target recipes for each one of the intermediatetarget scales and the final target scale. All other aspects illustratedfor the two-scale translation, when applicable, can be implemented forthe train scaling, such as outputting the target recipe(s) and alsooutputting the acceptability scores and the predicted trajectories, aswell as the feedback mechanisms.

A recap of the method for train scaling for the case of one intermediatetarget scale is found below, however it is clear that it can be extendedto any number of intermediate target scales.

A computer-implemented method of scaling a production process to producea chemical, pharmaceutical and/or biotechnological product, the scalingbeing from a source scale to an intermediate target scale to a finaltarget scale, wherein the production process is defined by a pluralityof steps specified by one or more process parameters controlling anexecution of the production process, the method comprising:

-   -   retrieving:        -   parameter evolution information that describes the time            evolution of the process parameter(s);        -   a plurality of recipe templates, wherein:            a recipe comprises the plurality of steps defining the            production process, and            a recipe template is a recipe in which at least one of the            process parameters specifying the plurality of steps is a            parameter being variable and having no predetermined value            at the outset;    -   receiving:        -   a source setup specification of a source setup to be used            for executing the production process at the source scale,            the source setup specification comprising the source scale            value;        -   an intermediate target setup specification of an            intermediate target setup to be used for executing the            production process at the intermediate target scale, the            intermediate target setup specification comprising the            intermediate target scale value;        -   a final target setup specification of a final target setup            to be used for executing the production process at the final            target scale, the final target setup specification            comprising the final target scale value;        -   a source recipe defining the production process at the            source scale;        -   at least one acceptability function defining conditions on            the values of the process parameter(s) when the values are            considered singularly at any one of the source scale,            intermediate target scale and final target scale and/or when            the values at any one scale are considered in relation to            corresponding values at any another one or more scales;    -   simulating the execution of the production process at the source        scale using the source setup specification, the source recipe        and the parameter evolution information;    -   determining, from the simulation, one or more source        trajectories for the process parameter(s), wherein a trajectory        corresponds to a time-based profile of values recordable during        the simulated execution of the production process;    -   performing a target determination step comprising:        -   selecting a recipe template pertinent to the production            process out of the plurality of recipe templates;        -   providing a first input value for the at least one variable            parameter in the selected recipe template;        -   providing a second input value for the at least one variable            parameter in the selected recipe template;        -   simulating the execution of the production process at the            intermediate target scale using the intermediate target            setup specification, the selected recipe template, the first            input value for the at least one variable parameter and the            parameter evolution information;        -   determining, from the simulation, one or more intermediate            target trajectories for the process parameters;        -   simulating the execution of the production process at the            final target scale using the final target setup            specification, the selected recipe template, the second            input value for the at least one variable parameter and the            parameter evolution information;        -   determining, from the simulation, one or more final target            trajectories for the process parameters;        -   making a first, a second and a third pairwise comparison            between any two of the source trajectory(ies), the            intermediate target trajectory(ies) and the final target            trajectory(ies), and making a three-wise comparison among            the source trajectory(ies), the intermediate target            trajectory(ies) and the final target trajectory(ies);        -   computing an acceptability score based on at least two            comparisons and on the at least one acceptability function;        -   computing a first optimal value and a second optimal value            for the at least one variable parameter by optimising the            acceptability score and/or computing a first acceptable            range and a second acceptable range for the at least one            variable parameter, wherein values within the first            acceptable range and values within the second acceptable            range yield an acceptability score above a specific            threshold;    -   if there is at least another pertinent recipe template,        repeating the target determination step for at least another        pertinent recipe template;    -   selecting at least one of the plurality of recipe templates and        corresponding computed value(s) for variable parameter(s) as        target recipe based on the acceptability scores computed for one        or more of recipe templates.

In particular, computing an acceptability score may comprise computingany combination of:

-   -   a first acceptability score for the selected recipe template        based on the first pairwise comparison and on the at least one        acceptability function;    -   a second acceptability score for the selected recipe template        based on the second pairwise comparison and on the at least one        acceptability function;    -   a third acceptability score for the selected recipe template        based on the third pairwise comparison and on the at least one        acceptability function;    -   a fourth acceptability score for the selected recipe template        based on the three-wise comparison and on the at least one        acceptability function;        -   and computing the optimal values/acceptable ranges may            comprise:            computing a first optimal value and a second optimal value            for the at least one variable parameter in the selected            recipe template by simultaneously optimising at least two of            the first acceptability score, the second acceptability            score, the third acceptability score and the fourth            acceptability score and/or            computing a first acceptable range and a second acceptable            range for the at least one variable parameter, wherein            values within the first acceptable range yield any one of a            first acceptability score, a second acceptability score, a            third acceptability score and a fourth acceptability score            above a respective first, second, third or fourth specific            threshold and values within the second acceptable range            yield any other one of a first acceptability score, a second            acceptability score, a third acceptability score and a            fourth acceptability score above a respective first, second,            third or fourth specific threshold.

In order to better illustrate how the train scaling method is just anextension of the two-scale method, reference is made to the followingwording.

The computer-implement method for translating from a source scale to atarget scale, wherein:

-   -   the target scale is a final target scale;    -   there is an intermediate target scale;    -   the at least one acceptability function further or alternatively        defines conditions for the values of the process parameters at        the intermediate target scale and/or values of the process        parameters at any two scales in relation to each other and/or        values of the process parameters at all scales in relation to        each other;    -   the input value for the at least one variable parameter is a        first input value;    -   the comparison between the source trajectory(ies) and the final        target trajectories is a first pairwise comparison;    -   the optimal value for the at least one variable parameter is a        first optimal value and the acceptable range for the at least        one variable parameter is a first acceptable range;        -   wherein the method further comprises:    -   retrieving an intermediate target setup specification of an        intermediate target setup to be used for executing the        production process at the intermediate target scale, the        intermediate target setup specification comprising the        intermediate target scale value; and        -   the target determination step further comprises:    -   providing a second input value for the at least one variable        parameter in the selected recipe template;    -   simulating the execution of the production process at the        intermediate target scale using the target setup specification,        the selected recipe template, the second input value for the at        least one variable parameter and the parameter evolution        information;    -   determining, from the simulation, one or more intermediate        target trajectories for the process parameters;    -   making a second pairwise comparison between the source        trajectory(ies) and the intermediate target trajectory(ies) and        a third pairwise comparison between the intermediate target        trajectory(ies) and the final target trajectory(ies), and making        a three-wise comparison among the source trajectory(ies), the        intermediate target trajectory(ies) and the final target        trajectory(ies);        -   wherein computing the acceptability score is performed based            on any combination of the first, second, third pairwise            comparison and three-wise comparison, and wherein the first            optimal value is computed simultaneously/together with a            second optimal value for the at least one variable parameter            in the selected recipe template by optimising the            acceptability score and/or    -   the first acceptable range is computed simultaneously/together        with a second acceptable range for the at least one variable        parameter, wherein values within the first acceptable range and        values within the second acceptable range yield an acceptability        score above a specific threshold.

In another aspect of the invention, a computer-implemented method ofscaling a state of a production equipment for a production process toproduce a chemical, pharmaceutical and/or biotechnological product, thescaling being from a source scale to a target scale, wherein the stateis defined by a set of state parameters describing a condition and/or abehaviour of the production equipment, is provided. This method is alsocalled instantaneous or time-point scaling and achieves the object ofidentifying best match parameters for a given time-point between aprocess at one scale and at another scale.

A state of the production equipment may indicate a static or dynamiccondition, e.g. how much a bioreactor is filled, and/or a behaviour ofthe production equipment, e.g. the stirring speed at which an impelleroperates. The state parameters are quantities that define suchcondition/behaviour, in particular by quantifying it when assuming agiven value. Since the state of the production equipment is alsoinfluenced by its environment, in some cases the state parameters mayinclude parameters that relate indirectly to the production equipment bydescribing the environment.

The state parameters may be set e.g. by an operator or by a controlsystem. Exemplarily, the state parameters for a bioreactor may includebut are not limited to: stir speed, gassing, fill volume and indicationsabout which gas to add. In particular, the state parameters may coincideor be a proper subset of the recipe parameters defined above.

Although in the following it will be mostly referred to a plurality ofstate parameters, the set may also have dimension 1, namely there may beonly one state parameter.

The method comprises retrieving mapping information that describes howthe state parameters relate to a set of derived parameters.

The mapping information may include theoretical equations and/or fittedrelations that links one or more state parameters to one or more derivedparameters. While the state parameters relate rather to how theproduction equipment is setup, the derived parameters relate more to theproduction equipment in the context of the production process beingperformed with it. In other words, the derived parameters may bequantities that arise during the production process and that may not becontrolled by an external input.

Exemplarily, the derived parameters for a bioreactor may include but arenot limited to one or more: tip speed, k_(L)a, mixing time, power input,Reynold's number, Froude number, minimum eddy size and superficial gasvelocity. In particular, the derived parameters may coincide or be aproper subset of the dynamic parameters defined above.

It should be noted that the derived parameters provide a staticdescription, i.e. their values are considered at a given time point andnot as they evolve during the process. The derived parameters thereforerepresent a “snapshot” of a process. In scale translation in this modeof operation, a user may make use of a range of time-points, or“snapshots” within the process, and ensure accuracy of scale translationin all of these.

Also the set of derived parameters may have dimension 1.

The mapping information may exemplarily be dependent on the specificproduction equipment, so that it may comprise a plurality of relationsbetween e.g. the same state parameter and the same derived parameter,with each different relation applying to a specific productionequipment.

The mapping information may be retrieved in any of the mannerillustrated to the parameter evolution information.

The method further comprises receiving:

-   -   a source setup specification of a source setup used for        executing the production process at the source scale, the source        setup specification comprising the source scale value;    -   a target setup specification of a target setup used for        executing the production process at the target scale, the target        setup specification comprising the target scale value;    -   a first set of state parameters at the source scale;    -   a second set of state parameters at the target scale, wherein at        least one of the state parameters at the target scale is a        parameter being variable and having no predetermined value at        the outset;    -   at least one acceptability function defining conditions on the        values of the state parameter(s) and/or the values of the        derived parameter(s) at the source scale and/or at the target        scale.

The setup specifications are the same as those discussed for the recipescaling above. Also the scope and form of the acceptability function(s)is the same as discussed above, in particular there may be relative andabsolute acceptability functions. Relative acceptability functions maydefine conditions on values of the state parameters at the source scaleand at the target scale and/or conditions on values of the derivedparameters at the source scale and at the target scale.

Further, a first set of state parameters (i.e. the values thereof) isprovided, which describes the state of the production equipment at thesource scale. Similarly, a second set of state parameters for the targetscale is provided. However, the state at the target scale is notcompletely determined, since one of the objectives of the method is tofind one or more optimal states at the target scale that correspond tothe state at the source scale. Accordingly, at least one of the stateparameters for the target scale in the second set is left free, in thesense that its value or their values is not fixed but can be modified inorder to arrive at the best solution. The role of the variableparameters in the set of state parameters is the same as the role of thevariable parameters in the recipe templates above.

The method further comprises calculating a first set of derivedparameters at the source scale using the first set of state parameters,the source setup specification and the mapping information; providing aninput value for the at least one variable parameter in the second set ofstate parameters; calculating a second set of derived parameters at thetarget scale using the second set of state parameters, the input value,the target setup specification and the mapping information.

As explained, the mapping information allows to derive the derivedparameters from the state parameters. Accordingly, using the mappinginformation and the source setup specification, a first set of derivedparameters at the source scale can be calculated. The calculation may beperformed analytically or by means of a numerical simulation.

The source setup specification may be needed for selecting theappropriate relations for the given source production equipment. Furtheror alternatively, some of the derived parameters may be obtained basedon features/quantities contained in the setup specification, such asregarding the geometry of the production equipment.

Similarly, the second set of state parameters is used together with oneor more “guessed” input values for the one or more variable parametersto derive a second set of derived parameters.

The method further comprises comparing the first set of state parameterswith the second set of state parameters and/or comparing the first setof derived parameters and the second set of derived parameters.

The comparison may e.g. comprise calculating the difference between astate parameter in the second set, i.e. at the target scale, and thesame parameter in the first set, i.e. at the source scale. The sameholds for the derived parameters. Other quantitative assessments of thecomparison may be performed, such as taking a relative difference, i.e.absolute value of (value in source−value in target)/(maximum (value insource, value in target)), a ratio of the values or combining the valuesaccording to given relations.

Finally, the method comprises computing, based on the comparison and onthe at least one applicable acceptability function, an acceptabilityscore for the second set of state parameters; computing an optimal valuefor the at least one variable parameter by optimising the acceptabilityscore and/or computing an acceptable range for the at least one variableparameter, wherein values within the acceptable range yield anacceptability score above a specific threshold.

The optimisation of the acceptability score function corresponds to theone described for the recipe scaling. In this case, the acceptabilityscore function is a function of the at least one variable parameter inthe second set of state parameters. Since there are no trajectories, thecomputation of the acceptability score is simplified.

If there is a plurality of acceptability functions and a plurality ofvariable parameters, computing the acceptability score comprises:

for each pertinent variable parameter of the plurality of variableparameters obtaining a partial acceptability score by:

-   -   selecting one or more applicable acceptability functions;    -   for each applicable acceptability function calculating an        acceptability value;    -   if there is a single applicable acceptability function, setting        the acceptability value to the partial acceptability score;    -   if there is a plurality of applicable acceptability functions,        aggregating the acceptability values for all applicable        acceptability functions to obtain the partial acceptability        score; and aggregating the partial acceptability scores for all        variable parameters to obtain the acceptability score.

Aggregating can be done as explained previously, e.g. by taking thearithmetic or geometrical mean.

One of the results of the method is, thus, an optimised set of stateparameters for the target scale. This may be output and e.g.automatically fed to a control system for setting up the productionequipment at the target scale. Other results that may be output includethe second set of derived parameters at the target scale and theacceptability score.

It is evident that the time-point scaling may be described as a specialcase of the recipe scaling, with some special conditions applying. Inparticular, the parameter evolution information includes only relationsbetween parameters and no equations for time evolution, the recipes andrecipe templates only comprise parameters but no process steps.

Thus, the same principles explained above apply for extending thetime-point scaling to a train scaling. In the particular case of oneintermediate target scale, the method would comprise:

-   -   retrieving mapping information that describes how the state        parameters relate to a set of derived parameters;    -   receiving:        -   a source setup specification of a source setup used for            executing the production process at the source scale, the            source setup specification comprising the source scale            value;        -   an intermediate target setup specification of an            intermediate target setup to be used for executing the            production process at the intermediate target scale, the            intermediate target setup specification comprising the            intermediate target scale value;        -   a final target setup specification of a final target setup            used for executing the production process at the final            target scale, the final target setup specification            comprising the final target scale value;        -   a first set of state parameters at the source scale;        -   a second set of state parameters at the intermediate target            scale, wherein at least one of the state parameters at the            intermediate target scale is a first parameter being            variable and having no predetermined value at the outset;        -   a third set of state parameters at the final target scale,            wherein at least one of the state parameters at the final            target scale is a second parameter being variable and having            no predetermined value at the outset;        -   at least one acceptability function defining constraints on            the values of the state parameter(s) and/or the values of            the derived parameter(s) at the source scale and/or at the            intermediate target scale and/or at the final target scale;    -   calculating a first set of derived parameters at the source        scale using the first set of state parameters, the source setup        specification and the mapping information;    -   providing a first input value for the at least one first        variable parameter in the second set of state parameters;    -   calculating a second set of derived parameters at the        intermediate target scale using the second set of state        parameters, the first input value, the intermediate target setup        specification and the mapping information;    -   providing a second input value for the at least one second        variable parameter in the third set of state parameters;    -   calculating a third set of derived parameters at the final        target scale using the third set of state parameters, the second        input value, the final target setup specification and the        mapping information;    -   making a plurality of pairwise comparisons within all pairs of        any two of the first, second and third set of state parameters        and/or within all pairs of any two of the first, second and        third set of derived parameters, and making at least one        three-wise comparison among the first, second and third set of        state parameters and/or among the first, second and third set of        derived parameters;    -   computing an acceptability score based on at least two        comparisons and on the at least one acceptability function;    -   computing a first optimal value for the at least one first        variable parameter and a second optimal value for the at least        one second variable parameter by optimising the acceptability        score and/or        computing a first acceptable range for the at least one first        variable parameter and a second acceptable range for the at        least one second variable parameter, wherein values within the        first acceptable range and values within the second acceptable        range yield an acceptability score above a specific threshold.

It is clear that the above method can be generalised to any arbitrarynumber of intermediate target scales.

According to another aspect, a computer program product is provided. Thecomputer program product comprises computer readable instructions,which, when loaded and executed on a computer system, cause the computersystem to perform operations as described above. The computer programproduct may be tangibly embodied in a computer readable medium.

According to yet another aspect of the invention, a computer systemoperable to scale a production process to produce a chemical,pharmaceutical and/or biotechnological product from a source scale to atarget scale is provided. The production process is defined by aplurality of steps specified by one or more process parameterscontrolling an execution of the production process and the computersystem comprises:

-   -   a retrieving module configured to retrieve:        -   parameter evolution information that describes the time            evolution of the process parameter(s);        -   a plurality of recipe templates, wherein:            -   a recipe comprises the plurality of steps defining the                production process, and a recipe template is a recipe in                which at least one of the process parameters            -   specifying the plurality of steps is a parameter being                variable and having no predetermined value at the                outset;    -   a receiving module configured to receive:        -   a source setup specification of a source setup to be used            for executing the production process at the source scale,            the source setup specification comprising the source scale            value;        -   a target setup specification of a target setup to be used            for executing the production process at the target scale,            the target setup specification comprising the target scale            value;        -   a source recipe defining the production process at the            source scale;        -   at least one acceptability function defining conditions for            the values of the process parameter(s) at the source scale            and/or at the target scale; and    -   a computing module configured to:        -   simulate the execution of the production process at the            source scale using the source setup specification, the            source recipe and the parameter evolution information;        -   determine, from the simulation, one or more source            trajectories for the process parameter(s), wherein a            trajectory corresponds to a time-based profile of values            recordable during the simulated execution of the production            process;        -   perform a target determination step comprising:            -   selecting one of the plurality of recipe templates;            -   providing an input value for the at least one variable                parameter in the selected recipe template;            -   simulating the execution of the production process at                the target scale using the target setup specification,                the selected recipe template, the input value for the at                least one variable parameter and the parameter evolution                information;            -   determining, from the simulation, one or more target                trajectories for the process parameters;            -   comparing the source trajectory(ies) and the target                trajectory(ies);            -   computing, based on the comparison and on the at least                one acceptability function, an acceptability score for                the selected recipe template;            -   computing an optimal value for the at least one variable                parameter in the selected recipe template by optimising                the acceptability score and/or computing an acceptable                range for the at least one variable parameter, wherein                values within the acceptable range yield an                acceptability score above a specific threshold;    -   select at least one of the plurality of recipe templates as        target recipe based on the acceptability scores computed for one        or more recipe templates.

The computer system may particularly comprise a memory and a processorto operate the modules. The retrieving module, the receiving module andthe computing module may be separate entities or may at least partlyoverlap with each other.

Further, the computer system may be configured to interface with atarget control system and/or a source control system via a network,shared disk, or database system, wherein the control systems control theexecution of the production process in the real world at the source andtarget scales. The interface may particularly allow transfer of dataand/or commands.

In some examples, the computer system may coincide with at least one ofthe target control system and source control system.

The computer system above may also be suitable, mutatis mutandis, forcarrying out a recipe train scaling, a time-point scaling and atime-point train scaling as illustrated above.

In conclusion, the invention particularly provides a means of scalingthat treats the process as a whole, so that optimisation of a matchbetween scales does not favour parts of the process at the expense ofthe whole. This is the case both for recipe scaling and time-pointscaling. Further, means of assessing risk across potential operationalspace are provided by the acceptability score, rather than just optimalvalues for the process parameter at the target scale.

The acceptability functions particularly allow sensitivities to beexpressed, i.e. knowledge about what matters for the process parameters,and taken into account in the scaling process. Further, the parameterevolution information particularly brings together experimentalbioreactor data with cell culture models in order to accurately simulatea production process involving several process parameters.

Finally, it is possible to reconcile multiple scales at the same time.Indeed, a full scaling train can be optimised in a single shot, so that,at each stage of deployment onto hardware during scaling, there is agood established prospect of being able to proceed further in the eventof success.

The subject matter described in this application can be implemented as amethod or on a device, possible in the form of one or more computerprogram products. The subject matter described in the application can beimplemented in a data signal or on a machine readable medium, where themedium is embodied in one or more information carriers, such as a CDROM, a DVD ROM, a semiconductor memory, or a hard disk. Such computerprogram products may cause a data processing apparatus to perform one ormore operations described in the application.

In addition, subject matter described in the application can beimplemented as a system including a processor, and a memory coupled tothe processor. The memory may encode one or more programs to cause theprocessor to perform one or more of the methods described in theapplication. Further subject matter described in the application can beimplemented using various machines.

BRIEF DESCRIPTION OF THE DRAWINGS

Details of exemplary embodiments are set forth below with reference tothe exemplary drawings. Other features will be apparent from thedescription, the drawings, and from the claims. The drawings should beunderstood as exemplary rather than limiting, as the scope of theinvention is defined by the claims.

FIG. 1 shows a computer system for scaling a production process toproduce a chemical, pharmaceutical, or biotechnological product.

FIG. 2 shows a method for recipe scaling of a production process.

FIG. 3 shows a block diagram indicating inputs and outputs of the recipescaling.

FIG. 4 shows part of an exemplary input for recipe scaling.

FIG. 5 shows an acceptability score as a function of time.

FIG. 6 shows exemplary target trajectories.

FIG. 7 shows a method for time-point scaling of a state of a productionequipment.

FIG. 8 shows a block diagram indicating inputs and outputs of thetime-point scaling.

FIG. 9 shows part of an exemplary input for time-point scaling.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following text, a detailed description of examples will be givenwith reference to the drawings. It should be understood that variousmodifications to the examples may be made. In particular, one or moreelements of one example may be combined and used in other examples toform new examples.

FIG. 1 shows a computer system 10 for scaling a production process toproduce a chemical, pharmaceutical, or biotechnological product.

The computer system 10 may include a processing unit, a system memory,and a system bus. The system bus couples various system componentsincluding the system memory to the processing unit. The processing unitmay perform arithmetic, logic and/or control operations by accessing thesystem memory. The system memory may store information and/orinstructions for use in combination with the processing unit. The systemmemory may include volatile and non-volatile memory, such as a randomaccess memory (RAM) and a read only memory (ROM).

The computer system 10 may further include a hard disk drive for readingfrom and writing to a hard disk (not shown), and an external unit drivefor reading from or writing to a removable unit. The drives and theirassociated computer-readable media provide nonvolatile storage ofcomputer readable instructions, data structures, program modules andother data for the personal computer 920. The data structures mayinclude relevant data for the implementation of the method as describedabove.

A number of program modules may be stored on the hard disk, externaldisk, ROM or RAM, including an operating system (not shown), one or moreapplication programs, other program modules (not shown), and programdata. The application programs may include at least a part of thefunctionality as described below.

A user may enter commands and information into the computer system 10through input devices such as keyboard and mouse. A monitor or othertype of display device is also connected to the system bus via aninterface, such as a video input/output.

The computer system 10 may communicate with other electronic devices. Tocommunicate, the computer system 10 may operate in a networkedenvironment using connections to one or more electronic devices.

In particular, the computer system may interface and communicate with asource control system 20 and a target control system 30. The computersystem 10 may be operable, possibly in conjunction with other devices,to scale a production process.

The source control system 20 may be connected to a bioreactor 40constituting the source equipment for performing the production processat the source scale. Similarly, the target control system 30 may beconnected to a bioreactor 50 constituting the target equipment forperforming the production process at the target scale. Although thebioreactor 40 is shown as being smaller than the bioreactor 50, thesituation could be reversed.

The source control system 20 and the target control system 30 may belocated in the same production facility or in different productionfacilities at different locations. The source control system 20 and thetarget control system 30 may be different entities or may coincide, i.e.be a single entity (not shown).

The control systems 20, 30 and the computer system 10 may be located indifferent rooms of the same facility or in different buildings on acorporate campus. The computer system 10 may be a separate entity fromthe control systems 20, 30. In other examples (not shown) the computersystem 10 may coincide with at least one of the source control system 20and the target control system 30.

In some examples, a database 60 may be provided. The database may beconnected to a network, such that the database is accessible by multipledevices/users. The database may be implemented as a cloud database, i.e.a database that runs on a cloud computing platform. In other words, thedatabase may be accessible over the Internet via a provider that makesshared processing resources and data available to computers and otherdevices on demand. The database may be implemented using a virtualmachine image or a database service. The database may use an SQL basedor NoSQL data model.

The database 60 may store any of: sets of values for process parameters,recipes, recipe templates, parameter evolution information, setupspecifications, acceptability functions. The database 501 may beaccessible from the process control device 503 via the Internet.

Communications between the database 60 and the computer system 10 may besecured, e.g. via Internet protocol security (IPSEC) or other securityprotocols. A virtual private network (VPN) may also be used.

The database 60 may be hosted by a service provider, possibly on avirtual machine, and may be accessible by various users from multipleorganizations, possibly located in a variety of different geographiclocations around the world. Alternatively, the database 60 may be hostedlocally, e.g. in the computer system 10. Accordingly, the computersystem 10 and the database 60 may or may not be located in physicalproximity. In particular, the database 60 may be located in a locationthat is geographically distant (e.g. on another continent) from thecomputer system.

An example of a production process that is scaled by the computer system10 may be a fed-batch process comprising the following phases:

-   -   add media to bioreactor    -   condition (set temperature, pH)    -   add inoculum    -   allow to grow in batch phase (control pH, DO, temperature;        sample at intervals)    -   when nutrients exhausted, move to fed phase    -   allow to grow in fed phase (control pH, DO, temperature; sample        at intervals; supply additional nutrients)    -   harvest product.

In particular, the computer system 10 may perform scaling according totwo approaches: recipe scaling, in which whole processes are convertedbetween scales, and instantaneous or time-point scaling, in whichsettings at a given point in time during a process are converted betweenscales.

Recipe Scaling

FIG. 2 shows an exemplary method for recipe scaling of a productionprocess. The method will be described in conjunction with FIG. 3, whichshows a block diagram indicating inputs and outputs of the recipescaling.

A production process is defined by a plurality of steps specified by aplurality of process parameters, comprising recipe parameters anddynamic parameters. In the following, the method will be described for aproduction process in a bioreactor that is to be scaled from a sourcescale to a target scale.

Examples of recipe parameters include but are not limited to: stir speed(rpm), fill volume (L), total gassing rate (L/hr), gas percentage ofO2(%), gas percentage of CO2(%) and parameters defining gassing profile,filling profile, temperature profile, inoculation and inductioncharacteristics and sampling pattern. Profiles may be:

-   -   constant rate (e.g. constant rate feed)    -   exponential (e.g. feed exponentially increasing, which typically        roughly corresponds to what the organism will be doing)    -   polynomial (e.g. order 3 polynomial to correspond to a        particular organism growth pattern);        and may be parameterised by one or more recipe parameters.

Examples of dynamic parameters include but are not limited to: tip speed(mps), mixing time (s), k_(L)a (hr⁻¹), power input (W), power input pervolume (Wm⁻³), Reynold's number, Froude number, minimum eddy size (μm),superficial gas velocity, cell density, cell metabolic state metric,carbon source availability, nitrogen source availability, secondarynitrogen source availability, inhibitor (toxin) concentration, pH,dissolved oxygen (%), dissolved CO2(%).

Exemplary scenarios for a translation between scales may be thefollowing. A small scale process is established at Ambr® 250 scale andthe intention is to transfer the process as a single step to 50L forproduction of larger quantities of product to evaluate downstreamprocessing issues (e.g. purification/filtration). In another one, amanufacture process is established in organisation at a given largescale such as 50 L, and there is a need to scale down to e.g. Ambr® 15or Ambr® 250 scale to perform initial clone selection; the scaled-downprocess needs to be “representative” otherwise the wrong clones will beselected and production when scaled back up to large scale will becompromised.

The method starts at S101 and the first step at S103 is retrievingparameter evolution information 200 and recipe templates 201. In thecurrent implementation, by way of example, parameter evolutioninformation is retrieved from a set of hard-coded formulae in thesoftware, in conjunction with structured data in XML format stored on afile-system accessible to the software.

The parameter evolution information 200 characterises how the processparameters change with time, exemplarily including initial conditionsfor the process parameters and relations among process parameters. Theparameter evolution information 200 describes physical modelling of thebioreactor in terms of parameters describing the bioreactor state (e.g.of fill volume) or the state of the production as determined by thebioreactor (e.g. power per volume), and biological modelling of the cellculture in the bioreactor (e.g. in terms of growth, oxygen consumptionand pH depression).

In particular, the parameter evolution information 200 may compriserelations empirically derived from previous executions of the productionprocess and equations derived by theoretical models about the evolutionof the production process.

For example, the parameter evolution information 200 comprisesexperimental bioreactor data fittings, which are empirically derivedmappings between recipe parameters (such as stirring speed, gassing rateand fill volume) and dynamic parameters (such as mixing time, k_(L)a andpower input). The experimental bioreactor data fittings link two or moreprocess parameters to each other.

Additionally, the parameter evolution information comprisestheoretically-derived equations and starting points both for a cellculture model and a bioreactor physical model. Examples of startingpoints for the cell culture model may be: growth rate 0.02 hr-1,temperature optimum 36 with growth reduced by 80% for each degree away,pH optimum 7.4 with growth reduced by 50% for each 1/10th of a unitaway, specific 1C consumption rate scaled to a unit, growth ratesaturating function of 1C source. In an alternative implementation, thecell culture model may be an empirical statistical model.

The bioreactor physical model may cover fill volume, temperature,analyte concentrations, pH, k_(L)a, mixing time, power input anddissolved oxygen. Details about each of these process parameters areprovided in the following:

Fill Volume

The starting value for the fill volume is zero. Volume is accumulateddue to liquid addition and bolus liquid addition, and reduced due tosampling. Evaporation is not considered in the following, but it may be,for example, by implementing a standard evaporation model whereby inputgas is assumed to be devoid of water, and exit gas is assumed to besaturated (in the case with no condenser).

For a bolus addition of volume v_(b), the fill volume is considered toupdate instantaneously, so that:

v _(new) =v _(old) +v _(b)

For the reduction of volume due to sampling, with a sample of volumev_(s), the fill volume is considered to update instantaneously, so that:

v _(new) =v _(old) −v _(s)

During continuous liquid addition with a rate profile r(t), the fillvolume updates according to the expression:

$\frac{dv}{dt} = {r(t)}$

Temperature

The temperature is driven by:

-   -   an external temperature, modelling a heating/cooling jacket or        air stream;    -   temperature changes due to the supply of liquid.

Continuous changes to temperature depend on a single bioreactor-typeparameter, indicating the rate of heat transfer between the external andinternal temperature:

$\frac{dT_{int}}{dt} = {\frac{k_{T}}{V}\left( {{T_{ext}(t)} - T_{int}} \right)}$

where T_(int) is the liquid temperature, k_(T) the heat transfercoefficient, and T_(ext)(t) the external temperature.

The equations for temperature changes due to supply of liquid (eitherbolus supply or profiled supply) are analogous to those for analyteconcentrations (see below).

Analyte Concentrations

Analyte concentrations change due to liquid addition and bolus liquidaddition. For bolus liquid addition of volume v_(b), for any givenanalyte at concentration c_(b) in the bolus, the analyte concentrationis considered to update instantaneously, so that:

$c_{new} = \frac{{\nu_{old}c_{old}} + {\nu_{b}c_{b}}}{v_{old} + v_{b}}$

where V_(oid) is the volume prior to bolus liquid addition.

For continuous liquid addition with a rate profile r(t) with fill volumeexpressed as v(t), the concentration for any given analyte, c, atconcentration c_(a) in the supply liquid changes according to thefollowing expression:

$\frac{d\; c}{dt} = {\frac{\left( {c_{b} - c} \right)}{v(t)}{r(t)}}$

pH

The following is a simplified model of buffering which aims at givingrepresentative results without the need to specify the detailedbuffering properties of media in detail. This is achieved by trackingboth pH and buffering capacity as two distinct variables.

Liquid addition and bolus liquid addition both affect pH. Carbon dioxidemediated (or carbonic acid mediated) effects on pH are not yetconsidered, but there exist possible sets of theoretical equations forthese in the literature which could potentially be included. Thebuffering capacity of the media is considered analogous to an analyte,so follows the evolution equations for analyte concentrations givenabove.

For bolus liquid addition of volume v_(b) with pH p_(b) and bufferingcapacity b_(b) into medium with volume v_(m), pH p_(m) and bufferingcapacity b_(m), the new medium pH is approximated as:

$p_{m,{new}} = \frac{{b_{m}p_{m,{old}}\nu_{m}} + {b_{b}p_{b}v_{b}}}{{b_{m}v_{m}} + {b_{b}v_{b}}}$

Dissolved Oxygen

Dissolved oxygen changes continually in the bioreactor due to thetransfer of oxygen from the gas supply, as dictated by the k_(L)a; thatis:

$\frac{d\lbrack{DO}\rbrack}{dt} = {k_{L}{a\left( {O^{*} - \lbrack{DO}\rbrack} \right)}}$

where O* is the partial pressure of oxygen in the supply gas, relativeto that in air.

The biological model for the cell culture aims at enabling theparsimonious description of a large range of bioprocesses salient forstirred bioreactor culture. The following model does not address, forexample:

-   -   detailed metabolic aspects of the cells: these are relevant only        inasmuch as they affect the behaviour of the cells in terms of        interaction with the bioreactor or end product;    -   heterogeneity in the bioreactor: it is assumed that        consideration of heterogeneity is adequately handled by        penalising large mixing times in terms of utilities;    -   specifics and details of any individual process: the aim is to        obtain broad but approximate coverage rather than a high degree        of detail concerning a particular cell type or product, for        example.

The biological model does instead focus on:

-   -   bioreactor-relevant culture effects, such as pH depression or        elevation (which may trigger base or acid addition by the        bioreactor system) and oxygen utilisation (which may affect gas        flow or stir speed via the intermediary of a DO control loop);    -   end-product-relevant culture dynamics, such as modulation in        cell activity or large-scale changes in cell metabolism e.g. to        production and away from growth;    -   bioreactor-dependent culture effect, such as pH, DO or nutrient        concentration effects.

The model is structured in terms of a number “culture model processes”;these are combined additively to produce a system of ordinarydifferential equations. Each culture model process has a number ofconstituent parts that govern its overall rate, multiplied by a maximumrate constant for that process. The constituent parts are functions ofcritical variables, and the output of these functions is combinedmultiplicatively.

For example, the rate may be determined as a function of temperature, T,and primary carbon source concentration, c_(1C):

r=f _(T)(T)f _([c1c])(c _(1c))

The functions f_(T) and f_([DO]) are selected from a (small) repertoireof biologically salient forms with a maximum of unity and minimum ofzero. For example, temperature dependency might be described by

${f_{T}(T)} = {\exp \left( {- \frac{\left( {T - {37}} \right)^{2}}{3}} \right)}$

to indicate a temperature optimum at 37 degrees but with relativelysmall sensitivity to deviations from that temperature. Similarly,primary carbon source concentration dependency might be described by

$f_{c\; 1c} = \frac{c_{1c}}{{0.5} + c_{1c}}$

to indicate a saturating maximum with half of the maximum rate achievedfor a concentration of primary carbon source of 0.5 gL⁻¹.

This rate then determines the rate of change of a set of variablesaffected by the culture model process. For example, the rate may drivethe growth of cell density, such that:

$\frac{d\; \rho}{dt} = {\rho \cdot r}$

The full expression for cell growth rate would then become:

$\frac{d\; \rho}{dt} = {r_{g} \cdot {\exp \left( {- \frac{\left( {T - {37}} \right)^{2}}{3}} \right)} \cdot \frac{c_{1c}}{{0.5} + c_{1c}} \cdot \rho}$

In the above example, the culture model process has two dependencies (onprimary carbon source and temperature) but only a single effect (on cellgrowth rate). A single culture model process may have multiple effectse.g. on cell growth rate and on primary carbon source consumption, whichleads immediately to a system of equations.

$\frac{d\; \rho}{dt} = {r_{g} \cdot {\exp \left( {- \frac{\left( {T - {37}} \right)^{2}}{3}} \right)} \cdot \frac{c_{1c}}{{0.5} + c_{1c}} \cdot \rho}$$\frac{dc_{1c}}{dt} = {{- r_{c}} \cdot {\exp \left( {- \frac{\left( {T - {37}} \right)^{2}}{3}} \right)} \cdot \frac{c_{1c}}{{0.5} + c_{1c}} \cdot \rho}$

A culture model process may depend, in terms of its rate, on one or moredriving processes in a hierarchy. In this case, the rates of the drivingprocess may be added (for example, consider when the driving processesrelate to nutrient-dependent growth and production respectively, and thederived process is nutrient consumption), or multiplied. The specificsof the responses and the rates are covered in the following sections.

Culture Process Responses: Temperature

A culture process (such as growth, production, or quiescence/death) maydepend on media temperature. It is assumed that the process will beindependent of external (jacket, driving) temperature, except inasmuchas this modulates the media temperature. It is anticipated thattemperature dependence will typically comprise either a normaldistribution, as described above, or, more usually, an asymmetric normaldistribution i.e.

${M(x)} = \left\{ \begin{matrix}{\exp \left( \frac{- \left( {x - \mu} \right)^{2}}{\sigma_{r}} \right)} & {x \geq \mu} \\{\exp \left( \frac{- \left( {x - \mu} \right)^{2}}{\sigma_{l}} \right)} & {otherwise}\end{matrix} \right.$

For growth of Cho cells, for example, a normal distribution with μ=37and σ=2 would be a good starting point.

pH

A culture process (such as growth, production, or quiescence/death) maydepend on media pH. It is anticipated that pH dependence will typicallycomprise a normal or asymmetric normal distribution.

For growth of Cho cells, for example, a normal distribution with μ=7.4and σ=0.5 would be a good starting point.

DO

A culture process (such as growth, production, or quiescence/death) maydepend on dissolved oxygen saturation within the media. It isanticipated that DO dependence will reflect saturation or sigmoidalkinetics, i.e.

${M(x)} = \frac{x}{k + x}$ or${M(x)} = \frac{1}{1 + {\exp \left( {- \left( \frac{x - k_{crit}}{k_{sens}} \right)} \right)}}$

For growth of Cho cells, for example, saturation kinetics withk_(crit)=15% and k_(sens)=5% would be a good starting point.

Cell Metabolic State Response

The single metabolic state variable is used to summarise pertinentproperties of the metabolic state of the cells. The meaning of thisvariable will be culture dependent, but it is primarily intended tosummarise the cells' behaviour in terms of relative energetic commitmentto growth and production. For example:

-   -   metabolic state=−1 indicating commitment of 100% energy to        growth    -   metabolic state=1 indicating commitment of 100% energy to        production.

Simulation starts with metabolic state set to zero and the state remainswithin the interval from −1 to 1 exclusive.

Clearly this is a vast simplification of the dynamics in real culture.However, it suffices to model the effect of induction and hence knock-oneffects in terms of further amplification of the cell density (orotherwise).

The response of culture model processes related to production and growthare anticipated to take the form of a sigmoidal or reversed sigmoidal(i.e. one minus sigmoidal) respectively.

Cell Activity Response

The single cell activity state variable is used to summarise pertinentproperties of cell activity, in particular quiescence or recovery fromlag, whichever are applicable for the culture in question.

Simulation starts with cell activity set to zero, and the cell activityremains within the interval from −1 and 1 exclusive.

Where the culture exhibits a recovery component, it is anticipated thatgrowth exhibits a sigmoidal response to the cell activity statevariable, with other processes responsible for increasing the cellactivity; in this case high cell activity state indicates that cellshave largely recovered from, for example, defrosting.

Where the culture exhibits a death or quiescence component, it isanticipated that growth (and potentially also production) will exhibit areverse sigmoidal response to the cell activity state variable, withother processes responsible for increasing the cell activity; in thiscase high cell activity state indicates a large number of quiescent orsenescent cells.

Nutrient Responses

The interactions between cells and media are complex. Setting asideresponses to pH, which have already been covered, the biologicalresponse to the media can be caricatured as:

-   -   support of growth by the media e.g. due to the provision of        adequate essential nutritive components, typically a sufficient        carbon and nitrogen supply;    -   support of production (of final product) by the media, with the        same criteria;    -   inhibition of growth by the media e.g. due to the presence of        toxic media components;    -   promotion of cell senescence or death by the media e.g. due to        the presence of toxic media components;    -   promotion of recovery from lag phase e.g. due to the presence of        a supportive nutrient environment;    -   promotion of transition from growth to production.

In many cases these drivers can be modelled as saturation kinetic orsigmoidal responses to concentrations of particular nutrients e.g.

-   -   saturation kinetic response of growth to primary carbon source    -   saturation kinetic response of growth to primary nitrogen source    -   sigmoidal kinetic response of metabolic state change to inducer        concentration    -   either inverse sigmoidal or saturation kinetic response to toxic        product or toxins.

In some cases, nutrient responses depend primarily on the ratio of twocomponents of the media. This is particularly the case when multiplecarbon sources are present, and one is utilised in preference to theother.

In this case, the cell culture process rate depends on a function (suchas sigmoidal, saturation, or reverse sigmoidal) of the quotient of theconcentrations of the components.

Culture Process Effects: Growth Rate

Growth or death cell culture processes effect a change in the celldensity, p:

$\frac{d\; \rho}{dt} = {r_{q}R\; \rho}$

where r_(g) is the growth rate coefficient associated with the cellculture process, and its product with the rate of the cell cultureprocess (i.e. R, from the cell process responses) indicates the specificgrowth rate. A constant non-zero R will therefore result in exponentialgrowth (R>1), death (R<1) of the cell density.

The total change in cell density during culture simulation arises fromgrowth and death due to pertinent cell culture processes, change todilution (e.g. due to liquid bolus or profile liquid addition) andchange due to supply of inoculum (i.e. due to the addition of a liquidwith non-zero inoculum concentration).

pH Depression Rate

Cell culture processes may depress the media pH. The framework providestwo means of modelling depression of pH.

-   -   pH depression directly by a cell culture process    -   pH depression indirectly due to the consumption or production of        an acidic or basic media component due to a cell culture        process.

In cases where the cell culture dynamics are considered at a high level(e.g. with an arbitrary carbon source) it is more appropriate to takethe former approach.

In this case, pH is modulated according to the following expression:

$\frac{d\lbrack{pH}\rbrack}{dt} = \frac{r_{\lbrack{pH}\rbrack}R\; \rho}{B}$

where r_([pH]) is the pH depression coefficient for the cell cultureprocess, R is the rate of the cell culture process at a given time, andB is the specific buffering capacity of the medium.

O2 Consumption Rate

Growth, maintenance and other metabolic cellular activities consumeoxygen from the media. Cell culture processes that involve oxygen uptakemodulate the media DO as follows:

$\frac{d\lbrack{DO}\rbrack}{dt} = {r_{\lbrack{DO}\rbrack}R\; \rho}$

where r_([DO]) is the DO uptake coefficient for the cell culture processand R is the rate of the cell culture process at a given time.

CO2 Production Rate

Similarly, metabolic activity, and in particular oxidative metabolism,produces carbon dioxide. Cell culture processes that involve carbondioxide evolution modulate the media ppCO₂ as follows:

$\frac{d\left\lbrack {{pp}\; {CO}\; 2} \right\rbrack}{dt} = {r_{\lbrack{{ppCO}\; 2}\rbrack}R\; \rho}$

Cell Activity Modification Rate

As previously described, within the framework, cell activity statedescribes either cells moving out of a lag phase, or intoquiescence/senescence. The cell activity state is a measure of theactivity of the cells between −1 and 1 exclusive. To maintain the statewithin this interval, cell culture processes that modulate the state doso as follows:

$\frac{d\left( {\tanh (A)} \right)}{dt} = r_{A}$

where A is the cell activity state, r_(A) the cell culture process ratecoefficient for cell activity state, and R is the rate of the cellculture process at a given time. No cell-density dependent effect isassumed, as the activity state is considered to apply homogeneously forall cells in the culture.

Cell Metabolic State Modification Rate

Cell metabolic state modification mirrors that for cell activitymodification, that is:

$\frac{d\left( {\tanh (M)} \right)}{dt} = {r_{M}R}$

where M is the cell metabolic state, r_(M) the cell culture process ratecoefficient for cell metabolic state, and R as above.

Nutrient Production Rates

A cell culture process may produce or consume components of the media.For example:

-   -   cell growth and maintenance typically consume a carbon source        and potentially a distinct nitrogen source;    -   cell metabolism may produce product    -   cell metabolism may produce toxic by-products.

Any given cell culture process may have zero or more nutrient productionrate coefficients, each of which dictates that for the nutrient, i, inquestion:

$\frac{{dC}_{i}}{dt} = {r_{Ci}R\; \rho}$

where r_(ci) is the rate coefficient.

Illustrative Culture Processes

The following illustrative culture process demonstrates many of theaspects of the framework described above.

Cell culture process Responses Effects Comment Cell Temperature: Growthrate: 0.5 Growth depends on growth normal(37, 1) pH depression correcttemperature, pH: normal(7, 0.5) rate: 1 pH, and adequate dO: o2consumption dO. It causes sigmoidal(10, 0.1) rate: 5e3 increase in cellmet. state: rev, 1C source: −1 count sigmoidal(0.5, 5) and depresses pHact. state: rev, and oxygen. It also sigmoidal(0.5, 5) requires and 1Csource: consumes the saturation(0.1) primary carbon source. Cell-deathProduct: act. state: 1 Product causes cell saturation(10) death,modelled as a change in the cell activity state Production Temperature:pH depression Complements normal(37, 1) rate: 1 growth pH: normal(7,0.5) o2 consumption depending on dO: rate: 5e3 metabolic state.sigmoidal(10, 0.1) 1C source: −1 Does not increase met. state Product: 1cell count, but does sigmoidal(0.5, 5) increase product act state: rev.titre. sigmoidal(0.5, 5) 1C source: saturation(0.1) Induction Inducer:met. state: 1 Presence of inducer saturation:0.001 causes cells tochange metabolic state.

Further, recipe templates 201 are retrieved at step S103. Recipes can beseen as a set of instructions dictating how the bioreactor behaves overtime. Recipe templates are considered to be recipes with free orvariable parameters. These variable parameters may result in verydifferent recipes being produced from a given template (for example, ifa path within a template is contingent on a free variable). Recipetemplates may contain calculations based on the variable parameters, aswell as on other process parameters within the process which they arerunning. Any of the recipe parameters listed above may be a variableparameter. For examples, profile parameters A, B and C for a feed rateprofiled as A+B t+C t², wherein t is time, may be left free to vary.

A library of recipe templates 201 may be retrieved, wherein differentrecipe templates may comprise different steps or instructions and/or mayhave different variable parameters.

Recipe templates 201 may comprise marks identifying phases of theproduction process, which are used together with acceptabilityfunctions, as explained below.

The acceptability functions 250 are received at step S105. In theexemplary implementation, the software supplies a user interface bywhich acceptability functions can be selected from a library and thenparameterised, or designed graphically. The library in this caseprovides acceptability functions indicating, amongst others, (a) a range(b) a single point (c) a normal distribution. For further examples seethe canonical forms below. The acceptability functions define conditionsfor the values of the process parameter(s) at the source scale and/or atthe target scale, in particular they define how acceptable these valuesare, when taken alone or in relation to one another. The value foracceptability may be a real number between 0 and 1, boundaries included.

There may be absolute and relative acceptability functions.

An absolute acceptability function maps from one or more processparameters at the same scale to an evaluation. Examples of absoluteacceptability functions define conditions for the following parameters:

-   -   Reynolds number (Rn): 0 for low Rn, increasing to 1 as Rn moves        into turbulent zone, then 1 afterwards;    -   k_(L)a: 0 for low k_(L)a, increasing as saturating function to 1        as k_(L)a increases;    -   mixing time: 1 for low mixing time, and then when mixing time        exceeds 20 s, equal to 20 s/mixing time (i.e. decreasing towards        0);    -   superficial gas velocity (SGV): 1 for low SGV, with sigmoidal        decline with increasing SGV for larger SGV, towards 0, to        reflect increased risk of foam with increasing SGV;    -   power per volume: normal distribution around some maximum;    -   stir speed: 0 for 0 . . . 5% and for 95 . . . 100% of bioreactor        stir speed, 1 otherwise (preferable to run with the system not        at its limits); or linear increase from 0 at 0% to 1 at 5%, then        flat until 95%, then linear decrease to 0 at 100%;    -   eddy size: 1 for eddy size greater than 5× organism size, then        linearly to 0 for 2× organism size, to reflect increasing risk        to organism as eddy size decreases;    -   product concentration at harvest time: 0 for 0, with saturating        increase (tends to 1 as product concentration tends to infinity)        as titre increases;    -   DO: 0 for 0 . . . 10%, then a sigmoidal curve between 10% and        20% up to 1, and then 1 for >20% to ensure adequate oxygen for        organism in sensitive region;    -   SGV+protein concentration: 1 for low SGV or low protein        concentration, decreasing to 0 as either become large; similarly        stir speed+SGV could also affect risk of foaming;    -   power per volume+cell density: normal distribution around some        optimum, but optimum shifts from low to large power per volume        as cell density increases (reflecting protective effect of cells        on other cells);    -   product concentration+product quality: 0 if either are 0, then        increasing as proportional to the product of concentration and        quality.

A relative acceptability function maps from a combination of (one ormore) process parameters at the source scale and corresponding (one ormore) process parameters at the target scale to an evaluation. Forexample, a way of combining two corresponding process parameters atdifferent scales is to compute their difference or relative difference,i.e. absolute value of (value in source−value in target)/(maximum (valuein source, value in target)). Examples of relative acceptabilityfunctions define the following conditions:

-   -   0 if mixing time is less at source scale than at target scale,        otherwise 1 (ensures no loss of mixing when moving up scales);    -   normal distribution of PPV around a delta of 0 (ensures PPV a        typical parameter for matching is conserved between scales);    -   1 for k_(L)a greater at target than source scale, otherwise        sigmoidal decrease to 0 as k_(L)a differs increasingly;    -   normal distribution around 0, standard deviation of 0.2 for cell        density (ensures growth curves conserved between source and        target).

Generally, the acceptability functions may be one-dimensional,two-dimensional or with higher dimensions. Some examples for canonicalforms for one-dimensional and two-dimensional acceptability functionsare reported below.

A one dimensional acceptability function can take one of the followingcanonical forms:

-   -   zero throughout, except at a given exact value, at which it is        one (this expresses a need to restrict the solution space to a        precise value, for example, if a particular fill volume is        needed at the small scale);    -   a normal distribution (this expresses the idea that a parameter        value such as k_(L)a has an optimum, but with some room for        manoeuvre around this); one within a range, zero outside that        range (this expresses the idea that a parameter value should        remain within this range, e.g. that mixing time should be less        than a given maximum)

A two dimensional acceptability function can take one of the followingcanonical forms:

-   -   f(x,y)=0 unless x=y, in which case f(x,y)=1 (this expresses the        need to find an exact match between scales e.g. for power input        per volume);    -   f(x,y)=0 unless x≥y, in which case f(x,y)=1 (this expresses the        need to restrict solution space to the situation where, at the        target scale, the parameter value exceeds that at the source,        for example in terms of oxygen transfer)    -   f(x,y)=0 unless x≤y, in which case f(x,y)=1 (this expresses the        need to restrict solution space to the situation where, at the        target scale, the parameter value is less than that at the        source, for example in terms of mixing time)    -   f(x,y)=N(x−y; m, s) (this expresses a benefit from having as        close possible but not necessarily a perfect match between the        scales e.g. for power per volume)    -   f(x,y)=N((x−y)/max(x,y); m, s) (as above, but ratiometric).

In the recipe scaling case, acceptability functions may be attached tospecified parts of process templates. For example, a process templatemight specify “start of batch” and “end of batch”, and an acceptabilityfunction would then be attached to the interval between these twowaypoints, and considered to apply only for those parts of the process.For example, in batch phase, only those acceptability functions may beapplied that do not set the acceptability value to depend on celldensity, because in batch phase there may be some variability as thecells grow up to use their nutrients up. Conversely, in fed phase, onlythose acceptability functions may be applied for which the acceptabilityvalue depends on cell density, because by start of fed phase,variability due to initial inoculum should have been “evened out”because they were all provided with same amount of nutrient in batchphase. In another example, those acceptability functions for which theacceptability value depends product titre (concentration) may be appliedonly in harvest phase, because titre is not relevant before the harvestpoint.

At step S105 also source setup specification 220 and target setupspecification 230 are received. In the exemplary implementation, theuser selects a source and target setup from a list. The softwareretrieves information concerning a given source or target setup,including the permitted configurations, minimum stir speed, maximum stirspeed, mixing properties, and so on, from structured data in an XMLfile, which is stored on the filesystem in a place accessible to thesoftware.

The setup specifications are description of the scales, namely of theequipment at source scale and target scale, specifying e.g. volumes andnumber/type of equipment components. A source setup specification 220may e.g. be Ambr® 250 with mammalian impeller and a target setupspecification 230 may e.g. be any of 2L UniVessel, 50L STR with 3+6impeller and combi sparger, 2000L STR with 3+6 impeller and combisparger.

Further, at step S105, also a recipe 240 for the source scale isreceived. In the exemplary implementation, the software provides a userinterface by which the user can design a recipe or recipe template bysuccessively adding and removing steps, and by parameterising steps. Thesoftware persists the recipe templates in an XML-based repository usingthe Microsoft .net serialiser to persist the object model for therecipes represented internally as objects within the softwareimplementation.

An example for the source recipe 240 may correspond to the followingprocedure: “Fill bioreactor with 0.2L of given media, heat to 35degrees, inoculate with clone to a density of 1e6 cells mL-1, incubatestirring at 600 rpm for 36 hrs controlling pH to 7.4 with bottom and topcontrol i.e. addition of acid or base as needed to push pH back to 7.4;maintain temperature; gas at a rate of 0.1 of total volume per minutewith air; feed with complex feed for 36 hrs continuing to monitor andcontrol pH, temperature; control DO with stirring and gassing, addinducer to trigger production. Harvest after 36 hrs.”

Then at step S107 the execution of the production process is simulatedat the source scale using a combination of the source setupspecification 220, the source recipe 240 and the parameter evolutioninformation 200. The source setup specification 220 provides a sort offramework for the simulation, while the source recipe 240 and theparameter evolution information 200 define how the process develops.

The physical, chemical and/or biological aspects of the productionprocess are simulated. In particular, the process simulation comprisespurely physical modelling (e.g. of fill volume), bioreactor modellingderived from physical characterisation of bioreactors (e.g. mapping fromfill volume and stir speed to power per volume), and biologicalmodelling of the organism (in terms of growth, oxygen consumption and pHdepression).

From the simulation at source scale, source trajectories for the processparameters are determined at S109. This means that the values of theprocess parameters are recorded at different times during thesimulation, so that a time dependence of the process parameters can bedetermined. The data points may be fitted to obtain fitting functionsfor the time evolution of the parameters. FIG. 6 shows examples oftrajectories, which will be discussed below.

Afterwards, at steps S111 and S113, a tentative target recipe is chosen.A recipe template is selected among the plurality of recipe templates210 and some input values are provided for the variable parameters inthe selected recipe template. The combination of the selected recipetemplate and the provided input values provides a tentative targetrecipe that can be used at step S115 for simulating the productionprocess at the target scale, similarly to step S107.

FIG. 4 shows part of an exemplary input for recipe scaling, in whichparameter evolution information 200, source recipe 240, input values fora selected recipe template 210 and acceptability functions 250 arevisible, while the source and target setup specifications are not shown.

Further, at step S117, target trajectories 270 corresponding to the timeevolution of process parameters at the target scale are determined,exactly as for the source trajectories.

Then at step S119 the initial guess for the variable parameters providedby the input values is modified to “best” satisfy the conditions givenby the acceptability functions 250. In particular, the simulation may berun multiple times to explore the space available for the variableparameter(s), until preferred points or surfaces in the space are found,i.e. the ones that make the target trajectories 270 most compliant withthe acceptability functions 250. The compliance of the targettrajectories 270 with the acceptability functions 250 is indicated by anacceptability score 280. Different degrees of compliance may be ofinterest, such as considering only the values of the variable parameterthat maximise the acceptability score 280 or considering also aplurality of values that yield an acceptability score 280 above acertain threshold. The plurality of values may form a single (possiblymulti-dimensional) range or non-adjacent ranges.

At step S121 it is checked whether there are other applicable recipetemplates that could be used as basis for a target recipe and, if so,steps S111-S115 are repeated.

Finally, at S123, one or more target recipes 260 are selected among thetested tentative target recipes, i.e. the recipe templates withcorresponding values for the variable parameters. The selection is basedon the acceptability score 280 and may be such that only the tentativetarget recipe with the highest acceptability score 280 is considered ormore tentative target recipes with an acceptability score 280 above acertain threshold are considered.

The method ends at S125.

In some cases, the target recipe(s) 260 may be output, e.g. as a file.An example for an output indicating the target recipe 260 may be: “onthe ambr 250, use recipe template named “ramp stir speed”, with theinitial inoculum set to 0.2% of the total volume and DO controlled to35% throughout”. The acceptability score 280 may also be output, e.g. astext “this will give a score of 80% of the optimal translation from your50L recipe” or as a plot in function of time, as shown in FIG. 5.Further, the simulated target trajectories 270 may be output e.g. asplots.

FIG. 5 shows an acceptability score as a function of time and FIG. 6shows exemplary target trajectories.

It can be seen that the acceptability score 280 in FIG. 5 has twotroughs around the 4th hour and the 6th hour. The advantage ofoutputting the target trajectories 270 is that causes for the poorperformance in these phases may be readily identified. For example,considering the target trajectories in the upper part of FIG. 6 for thecell density, it is apparent that the cell density evolves similarly atthe source scale and at the target scale. Since one of the objectives isto maximise the similarity between the processes at different scale, thecell density will have scored high. Instead, the target trajectories inthe lower part of FIG. 6 for k_(L)a show that the evolution of k_(L)a atthe target scale is not similar to the one at the source scale.Accordingly, k_(L)a may be at least partly the cause for the troughs inthe overall acceptability score 280.

To summarize, the method involves the mapping of a recipe at one scaleonto a recipe at a second scale subject to some constraints by means ofevaluating the trajectories that result from the simulation of therecipes at each of the scales in question. In particular, trajectoriesthat match best according to the acceptability functions are used toinfer values for appropriately populating a recipe template.

The method can be generalised to a train scaling involving an arbitrarynumber of scales, i.e. a translation that starts from a source scale andarrives at a final target scale by passing through and translating foreach of a plurality of intermediate target scales.

When applying the recipe scaling method of FIG. 2, setup specificationsfor the intermediate target scale(s) are received at S105 and theacceptability functions 250 cover all scales, i.e. define conditionsacross all scales. In addition, steps S113, S115 and S117 must beperformed also for each intermediate target scale. Further, step S119involves a simultaneous search for “best” values (optimal values orranges) for all target scales, i.e. the one or more intermediate targetscale and the final target scale.

An exemplary scenario for a train scaling may be going from Ambr® 15 toUniVessel® 2L to STR® 50L to SIR® 1000L to STR® 2000L. In particular,each of these scales may belong to a scale group as follows:

-   -   Configuration 1: Ambr® 250, attached to small scale group    -   Configuration 2: UniVessel® 2L, attached to small scale and        intermediate scale group    -   Configuration 3: STR® 50, attached to intermediate scale and        large scale group    -   Configuration 4: STR® 1000, attached to large scale group    -   Configuration 5: SIR® 2000, attached to large scale group.

The following acceptability functions 250 are defined for the groups:

1) Small Scale Group:

-   -   relative acceptability function: normal function of delta in        k_(L)a with mean 0, standard deviation 1 hr⁻¹;    -   relative acceptability function: normal function of delta in PPV        with mean 0, standard deviation 0.2 Wm⁻³;    -   absolute acceptability function: 0 valued when between 0 and 5%        and 95% and 100%, 1 otherwise (concerns the stir speed of        bioreactor as % of maximum);    -   absolute acceptability function: sigmoidal function with value 0        for 0, a sharp rise around 20 up to 1, for dissolved oxygen.

2) Intermediate Scale Group:

relative acceptability function: normal function of delta in PPV withmean 0, standard deviation 0.1 Wm⁻³

3) Large Scale Group:

-   -   relative acceptability function: normal function of delta in PPV        with mean 0, standard deviation 0.1 Wm⁻³        absolute acceptability function: 1 kW/(1 kW+power input)

It can be seen that, in the train scaling, particularly, theacceptability functions 250 link the different scales so that theprocess is scaled for each scale taking into account also therequirements that will arise for following scales.

A train scaling may be computationally expensive, however theacceptability functions can be used when exploring the space of thevariable parameters.

By way of example, a simple case with 3 target scales, A, B and C can beconsidered. A and B are linked by an acceptability function that dropsbelow 0.5 if the k_(L)a differs by more than 1 hr⁻¹, while anacceptability function linking B and C is such that if the k_(L)abetween B and C differs at all, it drops to 0, otherwise it is 1. Thevariable parameter is the stir speed, so the stir speed for scale A, thestir speed for scale B, and the stir speed for scale C need an inputvalue. A threshold of 0.5 is set for the final acceptability score, andthe acceptability score functions are combined by considering theproduct of the acceptability scores.

After an input value for the stir speed at scale A is chosen and aninput value for the stir speed at scale B is chosen, k_(L)a can becomputed based on the stir speed values and, in particular, the k_(L)adifference between A and B. There will only be certain regions for whichthe k_(L)a differs by less than 1 hr⁻¹ and, thus, the acceptabilityscore is greater than 0.5. Since acceptability scores are by definitionall lower or equal to 1, and since the acceptability score functions arecombined as a product, it can already be inferred that any values for Bthat are not in the above-mentioned region will result in a finalacceptability score lower than the threshold. Accordingly, a range offeasible input values for B is given on the basis of the input value forA.

In turn, having proposed a candidate stir speed for scale B, this can beimmediately propagated to scale C, because only that stir speed whichgives identical k_(L)a at scale C is acceptable (using the same logic asbefore).

The conclusion is that the optimisation should be mainly performed overthe value for A, and that the range for the B value for optimisation isdiminished as a function of the candidate stir speed for A, and thatthere is no need to optimise over C because it arises automatically fromB. This gives the appropriate set of clues to the optimiser to enable itto move rapidly to a solution, rather than exploring all possible valuesfor stir speed at A, B and C.

Time-Point Scaling

FIG. 7 shows an exemplary method for time-point scaling of a state of aproduction equipment. The method will be described in conjunction withFIG. 8, which shows a block diagram indicating inputs and outputs of thetime-point scaling.

In the following, the method will be described for a productionequipment including a bioreactor that may be used to perform any of theproduction processes discussed with reference to the recipe scaling.

Examples of bioreactor configurations include but are not limited to:Ambr® 15 fermentation, Ambr® 15 cell culture, Ambr® 250 mammalian, Ambr®250 microbial, UniVessel® 2L, STR® 50 with ring sparger and 2×3 bladeimpellers, STR® 200 with micro sparger and 3+6 blade impellers, and SIR®1000 with ring sparger and 2×3 blade impellers.

The state of a production equipment is defined by state parameters,which may include but are not limited to: stir speed (rpm), fill volume(L), total gassing rate (L hr⁻¹), gas percentage of O2(%) and gaspercentage of CO2(%).

The method starts at S701 and the first step at S703 is retrievingmapping information 800. In the exemplary implementation, mappinginformation is stored alongside bioreactor configuration in an XML filewhich is accessible to the software.

The mapping information 800 characterises how the state parametersrelate to derived parameters, which characterise a given point in timeduring the production process. The derived parameters include but arenot limited to: tip speed (mps), k_(L)a (hr⁻¹), mixing time (s), powerinput (W), power input per volume (W/m³), Reynold's number, Froudenumber, minimum eddy size (μm) and superficial gas velocity.

In particular, the mapping information 800 may comprise experimentalbioreactor data fittings derived from previous executions of theproduction process and/or equations derived by theoretical models.

The mapping information 800 may comprise different relations betweenstate parameters and derived parameters that apply to differentproduction equipment. Accordingly, in the retrieving step S703, only therelations appropriate for the case at hand may be retrieved.

For example, the time-point scaling may be applied between an Ambr® 250bioreactor and a UniVessel® 2L bioreactor. The retrieved mappinginformation 800 may include a mapping from stir speed, fill volume andgassing rate onto k_(L)a and PPV, as well as the equation linking stirspeed and tip speed for a given geometry of the bioreactor.

At step S705, source setup specification 810, target setup specification820 and acceptability functions 840 are received. In the exemplaryimplementation, source set up and target set up are specified throughthe user interface by the user, selecting from a combobox. The detailsof the source and target setup, in terms of the associated parametersand mapping, are stored in an XML file on the filesystem to which thesoftware has access.

The setup specifications are description of the scales, namely of theequipment at source scale and target scale, specifying e.g. volumes andnumber/type of equipment components. Continuing the example from above,the source setup specification 810 may be Ambr® 250 and a target setupspecification 840 may be UniVessel® 2L.

The acceptability functions 840 may have any of the canonical formsillustrated previously. For the above example, three acceptabilityfunctions 840 may be received: a relative acceptability function being anormal distribution with mean 0 hr⁻¹ and standard deviation 1 hr⁻¹ forthe difference between k_(L)a at the source scale and at the targetscale; a relative acceptability function being a normal distributionwith mean 0 s and standard deviation 5 s for the difference betweenmixing time at the source scale and at the target scale and an absoluteacceptability function requiring the tip speed at the target scale to be5% of the maximum in the bioreactor (i.e. 0 for tip speed lower than 5%and 1 for tip speed higher than 5%).

At step S705, also a first set of state parameters at the source scaleand a second set of state parameters at the target scale 830 isreceived. The first set of state parameters for the above example maybe: stir speed 400 rpm, gassing rate 0.02L min⁻¹, fill volume 0.2L, gas100% air. The second set of state parameters may be: fill volume 2L,gassing rate 0.2L min⁻¹, gas 100% air, with the stir speed left asvariable parameter, which will be populated later.

FIG. 9 shows part of an exemplary input for time-point scaling, in whichsource and target setup specifications 810 and 820, first and secondsets of state parameters 830, and acceptability functions 840 arevisible, while mapping information 800 is not shown.

Then at step S707 a first set of derived parameters for the source scaleis calculated. In the given example, k_(L)a and mixing time are computedat the ambr 250 scale, given stir speed 400 rpm, gassing rate 0.02Lmin⁻¹, fill volume 0.2L and gas 100% air and using the retrieved mappinginformation 800.

Then at step S709 input value(s) for the variable parameter(s) arechosen. For example, the input value for the stir speed at the targetscale may be 40 rpm, which is the midpoint of the minimum and themaximum stir speed for the UniVessel 2L. Using the input value(s), atstep S711 a second set of derived parameters 860 for the target scale iscalculated, similarly to step S707. Accordingly, in the given example,k_(L)a and mixing time are computed at the UniVessel® 2L scale, givenstir speed 40 rpm, gassing rate 0.2L min⁻¹, fill volume 2L and gas 100%air and using the retrieved mapping information 800. Further, the tipspeed is computed according to the mapping information 800 fromproperties of the UniVessel® 2L scale geometry and the candidate stirspeed 40 rpm.

Then at step S713 the initial guess for the variable parameters providedby the input values is modified to “best” satisfy the conditions givenby the acceptability functions 840. In particular, the space availablefor the variable parameter(s) is explored, until preferred points orsurfaces in the space are found, i.e. the ones that make the second setof state parameters most compliant with the acceptability functions 840.

The compliance of the state parameters at the target scale with theacceptability functions 840 is indicated by an acceptability score 870.Different degrees of compliance may be of interest, such as consideringonly the values of the variable parameter that maximise theacceptability score 870 or considering also a plurality of values thatyield an acceptability score 870 above a certain threshold. Theplurality of values may form a single (possibly multi-dimensional) rangeor non-adjacent ranges. If each acceptability function 840 gives apartial acceptability score, the total acceptability score may be givenby the product or the mean or other combinations of all partialacceptability scores.

The result is an optimised second set of state parameters 850 for thetarget scale.

In the given example, each time a new input value from the space of thestir speed is chosen, k_(L)a, mixing time and tip speed are derivedagain and the corresponding acceptability score is computed. The finalresult is, thus, an optimised stir speed or an acceptable range for thestir speed at the UniVessel 2L scale.

The method ends at S715.

The method can be generalised to a train scaling involving an arbitrarynumber of scales, i.e. a translation that starts from a source scale andarrives at a final target scale by passing through and translating foreach of a plurality of intermediate target scales.

When applying the time-point scaling method of FIG. 7, setupspecifications for the intermediate target scale(s) are received at S705and the acceptability functions 840 cover all scales, i.e. defineconditions across all scales. In addition, steps S709 and S711 must beperformed also for each intermediate target scale. Further, step S713involves a simultaneous search for “best” values (optimal values orranges) for all target scales, i.e. the one or more intermediate targetscale and the final target scale.

1. A computer-implemented method of scaling a production process toproduce a chemical, pharmaceutical and/or biotechnological product, thescaling being from a source scale to a target scale, wherein theproduction process is defined by a plurality of steps specified by oneor more process parameters controlling an execution of the productionprocess, the method comprising: retrieving: parameter evolutioninformation that describes the time evolution of the processparameter(s); a plurality of recipe templates, wherein: a recipecomprises the plurality of steps defining the production process, and arecipe template is a recipe in which at least one of the processparameters specifying the plurality of steps is a parameter beingvariable and having no predetermined value at the outset; receiving: asource setup specification of a source setup to be used for executingthe production process at the source scale, the source setupspecification comprising the source scale value; a target setupspecification of a target setup to be used for executing the productionprocess at the target scale, the target setup specification comprisingthe target scale value; a source recipe defining the production processat the source scale; at least one acceptability function definingconditions for the values of the process parameter(s) at the sourcescale and/or at the target scale; simulating the execution of theproduction process at the source scale using the source setupspecification, the source recipe and the parameter evolutioninformation; determining, from the simulation, one or more sourcetrajectories for the process parameter(s), wherein a trajectorycorresponds to a time-based profile of values recordable during thesimulated execution of the production process; performing a targetdetermination step comprising: selecting a recipe template pertinent tothe production process out of the plurality of recipe templates;providing an input value for the at least one variable parameter in theselected recipe template: simulating the execution of the productionprocess at the target scale using the target setup specification, theselected recipe template, the input value for the at least one variableparameter and the parameter evolution information; determining, from thesimulation, one or more target trajectories for the process parameters:comparing the source trajectory(ies) and the target trajectory(ies);computing, based on the comparison and on the at least one acceptabilityfunction, an acceptability score for the selected recipe template;computing an optimal value for the at least one variable parameter inthe selected recipe template by optimising the acceptability scoreand/or computing an acceptable range for the at least one variableparameter, wherein values within the acceptable range yield anacceptability score above a specific threshold; if there is at leastanother pertinent recipe template, repeating the target determinationstep for at least another pertinent recipe template; selecting at leastone of the plurality of recipe templates and corresponding computedvalue(s) for variable parameter(s) as target recipe based on theacceptability scores computed for one or more recipe templates.
 2. Thecomputer-implement method of claim 1, further comprising outputting thetarget recipe.
 3. The computer-implement method of claim 1, furthercomprising: providing the target recipe to a target control system;executing, by the target control system, the production process at thetarget scale based on the target recipe.
 4. The computer-implementmethod of claim 3, further comprising: evaluating the performance of theproduction process at the target scale; and modifying the at least oneacceptability function based on the evaluation.
 5. Thecomputer-implement method of claim 1, wherein a plurality ofacceptability functions is received and a plurality of targettrajectories are computed, and wherein computing the acceptability scorecomprises: for each target trajectory of the plurality of targettrajectories obtaining a second partial acceptability score by:selecting one or more applicable acceptability functions; for eachapplicable acceptability function performing a calculation step of:calculating an acceptability value based on the acceptability functionfor each time point in the target trajectories; aggregating theacceptability values at the different time points to obtain a firstpartial acceptability score; if there is a single applicableacceptability function, setting the second partial acceptability scoreto the first partial acceptability score; if there is a plurality ofapplicable acceptability functions, aggregating the first partialacceptability scores for all applicable acceptability functions toobtain the second partial acceptability score; and aggregating thesecond partial acceptability scores for all target trajectories toobtain the acceptability score.
 6. The computer-implement method ofclaim 1, further comprising: defining an aim quantity characterizing theproduction process at the source scale; executing, by a source controlsystem, the production process multiple times at the source scale whilevarying the process parameters and/or the process steps; selecting, fordefining the source recipe, a process based on the result for the aimquantity given by the process parameters and the process steps.
 7. Acomputer-implemented method of scaling a production process to produce achemical, pharmaceutical and/or biotechnological product, the scalingbeing from a source scale to an intermediate target scale to a finaltarget scale, wherein the production process is defined by a pluralityof steps specified by one or more process parameters controlling anexecution of the production process, the method comprising: retrieving:parameter evolution information that describes the time evolution of theprocess parameter(s); a plurality of recipe templates, wherein: a recipecomprises the plurality of steps defining the production process, and arecipe template is a recipe in which at least one of the processparameters specifying the plurality of steps is a parameter beingvariable and having no predetermined value at the outset; receiving: asource setup specification of a source setup to be used for executingthe production process at the source scale, the source setupspecification comprising the source scale value: an intermediate targetsetup specification of an intermediate target setup to be used forexecuting the production process at the intermediate target scale, theintermediate target setup specification comprising the intermediatetarget scale value; a final target setup specification of a final targetsetup to be used for executing the production process at the finaltarget scale, the final target setup specification comprising the finaltarget scale value; a source recipe defining the production process atthe source scale; at least one acceptability function definingconditions on the values of the process parameter(s) when the values areconsidered singularly at any one of the source scale, intermediatetarget scale and final target scale and/or when the values at any onescale are considered in relation to corresponding values at any anotherone or more scales; simulating the execution of the production processat the source scale using the source setup specification, the sourcerecipe and the parameter evolution information; determining, from thesimulation, one or mom source trajectories for the process parameter(s),wherein a trajectory corresponds to a time-based profile of valuesrecordable during the simulated execution of the production process;performing a target determination step comprising: selecting a recipetemplate pertinent to the production process out of the plurality ofrecipe templates; providing a first input value for the at least onevariable parameter in the selected recipe template; providing a secondinput value for the at least one variable parameter in the selectedrecipe template; simulating the execution of the production process atthe intermediate target scale using the intermediate target setupspecification, the selected recipe template, the first input value forthe at least one variable parameter and the parameter evolutioninformation; determining, from the simulation, one or more intermediatetarget trajectories for the process parameters; simulating the executionof the production process at the final target scale using the finaltarget setup specification, the selected recipe template, the secondinput value for the at least one variable parameter and the parameterevolution information; determining, from the simulation, one or morefinal target trajectories for the process parameters; making a first, asecond and a third pairwise comparison between any two of the sourcetrajectory(ies), the intermediate target trajectory(ies) and the finaltarget trajectory(ies), and making a three-wise comparison among thesource trajectory(ies), the intermediate target trajectory(ies) and thefinal target trajectory(ies); computing an acceptability score based onat least two comparisons and on the at least one acceptability function;computing a first optimal value and a second optimal value for the atleast one variable parameter by optimising the acceptability scoreand/or computing a first acceptable range and a second acceptable rangefor the at least one variable parameter, wherein values within the firstacceptable range and values within the second acceptable range yield anacceptability score above a specific threshold; if there is at leastanother pertinent recipe template, repeating the target determinationstep for at least another pertinent recipe template; selecting at leastone of the plurality of recipe templates and corresponding computedvalue(s) for variable parameter(s) as target recipe based on theacceptability scores computed for one or more recipe templates.
 8. Acomputer-implemented method of scaling a state of a production equipmentfor a production process to produce a chemical, pharmaceutical and/orbiotechnological product, the scaling being from a source scale to atarget scale, wherein the state is defined by a set of state parametersdescribing a condition and/or a behaviour of the production equipment,the method comprising: retrieving mapping information that describes howthe state parameters relate to a set of derived parameters; receiving: asource setup specification of a source setup used for executing theproduction process at the source scale, the source setup specificationcomprising the source scale value; a target setup specification of atarget setup used for executing the production process at the targetscale, the target setup specification comprising the target scale value;a first set of state parameters at the source scale; a second set ofstate parameters at the target scale, wherein at least one of the stateparameters at the target scale is a parameter being variable and havingno predetermined value at the outset; at least one acceptabilityfunction defining conditions on the values of the state parameter(s)and/or the values of the derived parameter(s) at the source scale and/orat the target scale; calculating a first set of derived parameters atthe source scale using the first set of state parameters, the sourcesetup specification and the mapping information; providing an inputvalue for the at least one variable parameter in the second set of stateparameters; calculating a second set of derived parameters at the targetscale using the second set of state parameters, the input value, thetarget setup specification and the mapping information; comparing thefirst set of state parameters with the second set of state parametersand/or comparing the first set of derived parameters and the second setof derived parameters; computing, based on the comparison and on the atleast one acceptability function, an acceptability score for the secondset of state parameters; computing an optimal value for the at least onevariable parameter by optimising the acceptability score and/orcomputing an acceptable range for the at least one variable parameter,wherein values within the acceptable range yield an acceptability scoreabove a specific threshold.
 9. A computer-implemented method of scalinga state of a production equipment for a production process to produce achemical, pharmaceutical and/or biotechnological product, the scalingbeing from a source scale to an intermediate target scale to a finaltarget scale, wherein the state is defined by a set of state parametersdescribing a condition and/or a behaviour of the production equipment,the method comprising: retrieving mapping information that describes howthe state parameters relate to a set of derived parameters; receiving: asource setup specification of a source setup used for executing theproduction process at the source scale, the source setup specificationcomprising the source scale value; an intermediate target setupspecification of an intermediate target setup to be used for executingthe production process at the intermediate target scale, theintermediate target setup specification comprising the intermediatetarget scale value; a final target setup specification of a final targetsetup used for executing the production process at the final targetscale, the final target setup specification comprising the final targetscale value; a first set of state parameters at the source scale; asecond set of state parameters at the intermediate target scale, whereinat least one of the state parameters at the intermediate target scale isa first parameter being variable and having no predetermined value atthe outset; a third set of state parameters at the final target scale,wherein at least one of the state parameters at the final target scaleis a second parameter being variable and having no predetermined valueat the outset; at least one acceptability function defining conditionson the values of the state parameter(s) and/or the values of the derivedparameter(s) at the source scale and/or at the intermediate target scaleand/or at the final target scale; calculating a first set of derivedparameters at the source scale using the first set of state parameters,the source setup specification and the mapping information; providing afirst input value for the at least one first variable parameter in thesecond set of state parameters; calculating a second set of derivedparameters at the intermediate target scale using the second set ofstate parameters, the first input value, the intermediate target setupspecification and the mapping information: providing a second inputvalue for the at least one second variable parameter in the third set ofstate parameters; calculating a third set of derived parameters at thefinal target scale using the third set of state parameters, the secondinput value, the final target setup specification and the mappinginformation; making a plurality of pairwise comparisons within all pairsof any two of the first, second and third set of state parameters and/orwithin all pairs of any two of the first, second and third set ofderived parameters, and making at least one three-wise comparison amongthe first, second and third set of state parameters and/or among thefirst, second and third set of derived parameters; computing anacceptability score based on at least two comparisons and on the atleast one acceptability function; computing a first optimal value forthe at least one first variable parameter and a second optimal value forthe at least one second variable parameter by optimising theacceptability score and/or computing a first acceptable range for the atleast one first variable parameter and a second acceptable range for theat least one second variable parameter, wherein values within the firstacceptable range and values within the second acceptable range yield anacceptability score above a specific threshold.
 10. A computer programproduct comprising computer-readable instructions, which, when loadedand executed on a computer system, cause the computer system to performoperations according to the method of claim
 1. 11. A computer systemoperable to scale a production process to produce a chemical,pharmaceutical and/or biotechnological product from a source scale to atarget scale, wherein the production process is defined by a pluralityof steps specified by one or more process parameters controlling anexecution of the production process, the computer system comprising: aretrieving module configured to retrieve: parameter evolutioninformation that describes the time evolution of the processparameter(s); a plurality of recipe templates, wherein: a recipecomprises the plurality of steps defining the production process, and arecipe template is a recipe in which at least one of the processparameters specifying the plurality of steps is a parameter beingvariable and having no predetermined value at the outset; a receivingmodule configured to receive: a source setup specification of a sourcesetup to be used for executing the production process at the sourcescale, the source setup specification comprising the source scale value;a target setup specification of a target setup to be used for executingthe production process at the target scale, the target setupspecification comprising the target scale value; a source recipedefining the production process at the source scale; at least oneacceptability function defining conditions for the values of the processparameter(s) at the source scale and/or at the target scale; and acomputing module configured to: simulate the execution of the productionprocess at the source scale using the source setup specification, thesource recipe and the parameter evolution information: determine, fromthe simulation, one or more source trajectories for the processparameter(s), wherein a trajectory corresponds to a time-based profileof values recordable during the simulated execution of the productionprocess; perform a target determination step comprising: selecting arecipe template pertinent to the production process out of the pluralityof recipe templates; providing an input value for the at least onevariable parameter in the selected recipe template; simulating theexecution of the production process at the target scale using the targetsetup specification, the selected recipe template, the input value forthe at least one variable parameter and the parameter evolutioninformation; determining, from the simulation, one or more targettrajectories for the process parameters; comparing the sourcetrajectory(ies) and the target trajectory(ies); computing, based on thecomparison and on the at least one acceptability function, anacceptability score for the selected recipe template; computing anoptimal value for the at least one variable parameter in the selectedrecipe template by optimising the acceptability score and/or computingan acceptable range for the at least one variable parameter, whereinvalues within the acceptable range yield an acceptability score above aspecific threshold; if there is at least another pertinent recipetemplate, repeat the target determination step for at least anotherpertinent recipe template; select at least one of the plurality ofrecipe templates and corresponding computed value(s) for variableparameter(s) as target recipe based on the acceptability scores computedfor one or more recipe templates.
 12. The computer system of claim 11,further comprising an output module configured to output the targetrecipe.
 13. The computer system of claim 12, further configured to beinterfaced with a target control system for controlling a target processequipment, wherein: the computing module is further configured toprovide the target recipe to the target control system; and the targetcontrol system is configured to execute the production process at thetarget scale based on the target recipe.
 14. The computer system ofclaim 13, wherein the computing module is further configured to:evaluate the performance of the production process at the target scale;and modify the at least one acceptability function based on theevaluation.
 15. The computer system of claim 11, further configured tobe interfaced with a source control system for controlling a sourceprocess equipment, wherein: the source control system is configured toexecute the production process multiple times at the source scale whilevarying the process parameters and/or the process steps; and thecomputing module is configured to define an aim quantity characterizingthe process at the source scale and to select, for defining the sourcerecipe, a process based on the result for the aim quantity given by theprocess parameters and the process steps.